In [1]:
%matplotlib inline
import pandas as pd
import numpy as np
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from statsmodels.stats.multicomp import MultiComparison
from statsmodels.formula.api import ols
from scipy import stats
In [2]:
data = pd.read_csv("playlists.csv", sep=";", encoding = "ISO-8859-1")
In [3]:
data.describe(include="all")
Out[3]:
| company | playlist_sample | namesfiles | no | artist | song | sampleratefiles | totalsamplesfiles | durationfiles | bitratefiles | ... | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | attackslopefiles | attackleapfiles | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1782 | 1782.000000 | 1782 | 1782.000000 | 1782 | 1782 | 1782.0 | 1.782000e+03 | 1782.000000 | 1782.000000 | ... | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 |
| unique | 6 | NaN | 515 | NaN | 353 | 443 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| top | Arte Francés | NaN | 30 - Kasbo - Your Tempo.mp3 ... | NaN | Satin Jackets | Hula Hoop.mp3 ... | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| freq | 441 | NaN | 6 | NaN | 51 | 12 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| mean | NaN | 2.084175 | NaN | 17.116162 | NaN | NaN | 44100.0 | 1.043632e+07 | 236.651237 | 252.336700 | ... | 0.332301 | 0.319191 | 0.265246 | 0.440462 | 0.549565 | 0.581967 | 0.477825 | 0.430522 | 15.804409 | 0.507503 |
| std | NaN | 1.114796 | NaN | 11.837401 | NaN | NaN | 0.0 | 3.227105e+06 | 73.176981 | 88.377597 | ... | 0.270616 | 0.263919 | 0.249612 | 0.290454 | 0.314771 | 0.323173 | 0.321646 | 0.295563 | 9.338659 | 0.247587 |
| min | NaN | 1.000000 | NaN | 1.000000 | NaN | NaN | 44100.0 | 5.965054e+06 | 135.262000 | 128.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.168304 |
| 25% | NaN | 1.000000 | NaN | 8.000000 | NaN | NaN | 44100.0 | 8.353151e+06 | 189.413850 | 128.000000 | ... | 0.116591 | 0.109123 | 0.075378 | 0.204738 | 0.301961 | 0.316822 | 0.214680 | 0.203097 | 9.810711 | 0.285584 |
| 50% | NaN | 2.000000 | NaN | 15.500000 | NaN | NaN | 44100.0 | 9.480378e+06 | 214.974562 | 320.000000 | ... | 0.262799 | 0.259887 | 0.183082 | 0.396861 | 0.520957 | 0.570088 | 0.418077 | 0.379737 | 14.833864 | 0.452456 |
| 75% | NaN | 3.000000 | NaN | 24.000000 | NaN | NaN | 44100.0 | 1.146931e+07 | 260.075075 | 320.000000 | ... | 0.494897 | 0.469603 | 0.384485 | 0.642814 | 0.829318 | 0.918554 | 0.735384 | 0.616558 | 19.964413 | 0.730669 |
| max | NaN | 5.000000 | NaN | 65.000000 | NaN | NaN | 44100.0 | 2.843136e+07 | 644.702000 | 320.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 66.233620 | 0.999408 |
11 rows × 64 columns
Find positives and negatives songs of the process for every company.
In [4]:
companies = data['company'].unique()
by_company = [data[data.company == company] for company in companies]
positives = []
negatives = []
for data_com in by_company:
data_com = data_com.sort_values('playlist_sample')
last_pl = int(data_com.tail(1).playlist_sample)
#pls = pd.DataFrame({'pl':range (1,last_pl+1), 'old':[0]*last_pl, 'new':[0]*last_pl})
#curr_pl = data_com.query('playlist_sample == '+str(1))
#pls.new[0]=(curr_pl.shape[0])/3
#sum_pls = [curr_pl.shape[0]/3]
#for i in range(2,last_pl+1):
# curr_pl = data_com.query('playlist_sample == '+str(i))
# pre_pl = data_com.query('playlist_sample == '+str(i-1))
# olds = curr_pl['song'].map(pre_pl['song'].value_counts()).sum(axis = 0)/3
# pls.old[i-1]= olds/3
# pls.new[i-1]=(curr_pl.shape[0]-olds)/3
# sum_pls.append(curr_pl.shape[0]/3)
#sum_pls = pd.DataFrame(sum_pls)
#pls_rel = (pls[['old']].div(sum_pls.values, axis=0)*100).join(pls[['new']].div(sum_pls.values, axis=0)*100)
#pls[['old','new']].plot(kind='bar', stacked=True, title=data_com.iloc[0,0])
#for n in pls_rel:
# for i, (cs, ab, pc) in enumerate(zip(pls.iloc[:, 1:].cumsum(1)[n].values, pls[n].values, pls_rel[n].values)):
# if(pc>0):
# plt.text(i, cs - ab/2, str(np.round(pc, 1)) + '%', va='center', ha='center')
df_last_pl= data_com.query('playlist_sample == '+str(last_pl))
positives.append(df_last_pl)
pos_loc = pd.DataFrame({}, columns=data_com.columns)
rep=0
for index, row in data_com[data_com.playlist_sample<last_pl].iterrows():
if not ((df_last_pl['artist'] == row['artist']) & (df_last_pl['song'] == row['song'])).any() and pos_loc[(pos_loc['artist']== row['artist']) & (pos_loc['song'] == row['song'])].shape[0]<3:
pos_loc= pos_loc.append(row, ignore_index=True)
else:
rep+=1
#n_vs_p = pd.DataFrame({'sam':['pos', 'neg'],'num':[df_last_pl.shape[0]/3,pos_loc.shape[0]/3]})
# n_vs_p.plot.bar(x='sam', y='num', rot=0, title=data_com.iloc[0,0])
negatives.append(pos_loc)
In [5]:
df_n_ps = []
for i in range(len(negatives)):
negatives[i]['chosen']=0
positives[i]['chosen']=1
df_n_ps.append(negatives[i].append(positives[i]))
D:\Usuarios\1144084318\AppData\Roaming\Python\Python37\site-packages\ipykernel_launcher.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy after removing the cwd from sys.path.
ANOVA¶
In [6]:
import warnings
import matplotlib.pyplot as plt
import math
import seaborn as sns
warnings.filterwarnings('ignore')
In [27]:
alpha = 0.05
for df_n_p in df_n_ps:
df_n_p = df_n_p.fillna(0)
fig = plt.figure(figsize=(17,200))
i=1
for index in range(10,df_n_p.shape[1]-1):
print(name)
name= df_n_p.columns.values[index]
df_n_p[name]=df_n_p[name].astype('float64')
mc = MultiComparison(df_n_p[name], df_n_p['chosen'])
mc_results = mc.tukeyhsd()
if mc_results._results_table.data[1:][0][5]:
# print(name)
results = ols(name+' ~ C(chosen)', data=df_n_p).fit()
homogeneity_test = stats.levene(df_n_p[name][df_n_p['chosen'] == 0], df_n_p[name][df_n_p['chosen'] == 1])[1]
normality_test = stats.shapiro(results.resid)[1]
if homogeneity_test > alpha and normality_test > alpha:
# print(results.summary())
ax = fig.add_subplot(math.ceil(df_n_p.shape[1]-9/2), 2, i)
sns.kdeplot(df_n_p.loc[df_n_p.chosen==0][name], shade=True, ax=ax);
sns.kdeplot(df_n_p.loc[df_n_p.chosen==1][name], shade=True, ax=ax);
plt.title(df_n_p.iloc[0,0].upper()+" "+name)
plt.legend(['neg', 'pos'])
i+=1
kurtosisfiles rmsfiles rmsmedianfiles lowenergyfiles ASRfiles beatspectrumfiles eventdensityfiles tempofiles pulseclarityfiles zerocrossfiles rolloffsfiles brightnessfiles spreadfiles centroidfiles
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-27-4116f4441a3f> in <module> 9 df_n_p[name]=df_n_p[name].astype('float64') 10 mc = MultiComparison(df_n_p[name], df_n_p['chosen']) ---> 11 mc_results = mc.tukeyhsd() 12 if mc_results._results_table.data[1:][0][5]: 13 # print(name) ~\AppData\Roaming\Python\Python37\site-packages\statsmodels\sandbox\stats\multicomp.py in tukeyhsd(self, alpha) 1011 np.round(res[4][:, 0], 4), 1012 np.round(res[4][:, 1], 4), -> 1013 res[1]), 1014 dtype=[('group1', object), 1015 ('group2', object), ~\AppData\Roaming\Python\Python37\site-packages\statsmodels\compat\python.py in lzip(*args, **kwargs) 60 61 def lzip(*args, **kwargs): ---> 62 return list(zip(*args, **kwargs)) 63 64 def lmap(*args, **kwargs): TypeError: zip argument #4 must support iteration
In [7]:
from collections import Counter
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix, accuracy_score, silhouette_samples, silhouette_score, calinski_harabaz_score
from sklearn import preprocessing
from sklearn.decomposition import PCA
In [8]:
for i in range(len(companies)):
df_n_ps[i].bitratefiles = df_n_ps[i].bitratefiles.astype('float64')
df_n_ps[i].pitchfiles = df_n_ps[i].pitchfiles.astype('float64')
df_n_ps[i].bestkeyfiles = df_n_ps[i].bestkeyfiles.astype('float64')
df_n_ps[0].info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 315 entries, 0 to 179 Data columns (total 65 columns): company 315 non-null object playlist_sample 315 non-null object namesfiles 315 non-null object no 315 non-null object artist 315 non-null object song 315 non-null object sampleratefiles 315 non-null object totalsamplesfiles 315 non-null object durationfiles 315 non-null float64 bitratefiles 315 non-null float64 rmsfiles 315 non-null float64 rmsmedianfiles 315 non-null float64 lowenergyfiles 315 non-null float64 ASRfiles 315 non-null float64 beatspectrumfiles 315 non-null float64 eventdensityfiles 315 non-null float64 tempofiles 315 non-null float64 pulseclarityfiles 315 non-null float64 zerocrossfiles 315 non-null float64 rolloffsfiles 315 non-null float64 brightnessfiles 315 non-null float64 spreadfiles 315 non-null float64 centroidfiles 314 non-null float64 kurtosisfiles 315 non-null float64 flatnessfiles 315 non-null float64 entropyfiles 315 non-null float64 mfccfiles_1 315 non-null float64 mfccfiles_2 315 non-null float64 mfccfiles_3 315 non-null float64 mfccfiles_4 315 non-null float64 mfccfiles_5 315 non-null float64 mfccfiles_6 315 non-null float64 mfccfiles_7 315 non-null float64 mfccfiles_8 315 non-null float64 mfccfiles_9 315 non-null float64 mfccfiles_10 315 non-null float64 mfccfiles_11 315 non-null float64 mfccfiles_12 315 non-null float64 mfccfiles_13 315 non-null float64 pitchfiles 315 non-null float64 inharmonicityfiles 315 non-null float64 bestkeyfiles 315 non-null float64 keyclarityfiles 315 non-null float64 modalityfiles 315 non-null float64 tonalcentroidfiles_1 315 non-null float64 tonalcentroidfiles_2 315 non-null float64 tonalcentroidfiles_3 315 non-null float64 tonalcentroidfiles_4 315 non-null float64 tonalcentroidfiles_5 315 non-null float64 tonalcentroidfiles_6 315 non-null float64 chromagramfiles_1 315 non-null float64 chromagramfiles_2 315 non-null float64 chromagramfiles_3 315 non-null float64 chromagramfiles_4 315 non-null float64 chromagramfiles_5 315 non-null float64 chromagramfiles_6 315 non-null float64 chromagramfiles_7 315 non-null float64 chromagramfiles_8 315 non-null float64 chromagramfiles_9 315 non-null float64 chromagramfiles_10 315 non-null float64 chromagramfiles_11 315 non-null float64 chromagramfiles_12 315 non-null float64 attackslopefiles 315 non-null float64 attackleapfiles 315 non-null float64 chosen 315 non-null int64 dtypes: float64(56), int64(1), object(8) memory usage: 162.4+ KB
Vamos a reemplazar los NaN y entonces a normalizar los datos para que todas las variables tengan la misma importancia. Solo vamos a considerar los datos numéricos.
In [9]:
df_n_ps_std = [0]*len(companies)
for i in range(len(companies)):
df_n_ps[i] = df_n_ps[i].fillna(0)
df_n_ps_std[i] = pd.DataFrame(preprocessing.scale(df_n_ps[i].iloc[:,8:]))
df_n_ps_std[i].columns=df_n_ps[i].columns[8:]
df_n_ps_std[0].mean(axis=0)
Out[9]:
durationfiles -1.889141e-16 bitratefiles 0.000000e+00 rmsfiles 3.559763e-16 rmsmedianfiles -2.396672e-16 lowenergyfiles 1.543739e-16 ASRfiles 1.226532e-16 beatspectrumfiles 2.269789e-16 eventdensityfiles -6.132661e-17 tempofiles 4.103860e-16 pulseclarityfiles -6.696583e-17 zerocrossfiles -1.092600e-16 rolloffsfiles 2.661011e-16 brightnessfiles 1.092600e-16 spreadfiles 1.519067e-16 centroidfiles 1.501444e-16 kurtosisfiles 1.875043e-16 flatnessfiles -4.017950e-17 entropyfiles 6.012827e-16 mfccfiles_1 -4.398598e-16 mfccfiles_2 -2.326182e-17 mfccfiles_3 6.308886e-17 mfccfiles_4 1.718202e-17 mfccfiles_5 1.832749e-17 mfccfiles_6 -3.172066e-17 mfccfiles_7 -1.400996e-16 mfccfiles_8 5.110550e-17 mfccfiles_9 3.101575e-17 mfccfiles_10 -6.485112e-17 mfccfiles_11 -4.229421e-18 mfccfiles_12 -1.304071e-17 mfccfiles_13 -1.233581e-17 pitchfiles 0.000000e+00 inharmonicityfiles -1.009422e-15 bestkeyfiles 2.424868e-16 keyclarityfiles -3.972131e-16 modalityfiles -3.771234e-17 tonalcentroidfiles_1 -1.517745e-17 tonalcentroidfiles_2 -5.921189e-17 tonalcentroidfiles_3 2.326182e-17 tonalcentroidfiles_4 2.502407e-17 tonalcentroidfiles_5 3.260179e-17 tonalcentroidfiles_6 -2.361427e-17 chromagramfiles_1 6.414622e-17 chromagramfiles_2 -2.061843e-17 chromagramfiles_3 -3.489272e-17 chromagramfiles_4 -1.755210e-16 chromagramfiles_5 1.797504e-17 chromagramfiles_6 -3.101575e-17 chromagramfiles_7 -8.776049e-17 chromagramfiles_8 7.471977e-17 chromagramfiles_9 -4.194176e-17 chromagramfiles_10 3.630253e-17 chromagramfiles_11 5.057683e-17 chromagramfiles_12 -5.894756e-17 attackslopefiles -6.626093e-17 attackleapfiles -1.423905e-16 chosen 7.218212e-16 dtype: float64
In [10]:
df_n_ps_std[0].std(axis=0)
Out[10]:
durationfiles 1.001591 bitratefiles 0.000000 rmsfiles 1.001591 rmsmedianfiles 1.001591 lowenergyfiles 1.001591 ASRfiles 1.001591 beatspectrumfiles 1.001591 eventdensityfiles 1.001591 tempofiles 1.001591 pulseclarityfiles 1.001591 zerocrossfiles 1.001591 rolloffsfiles 1.001591 brightnessfiles 1.001591 spreadfiles 1.001591 centroidfiles 1.001591 kurtosisfiles 1.001591 flatnessfiles 1.001591 entropyfiles 1.001591 mfccfiles_1 1.001591 mfccfiles_2 1.001591 mfccfiles_3 1.001591 mfccfiles_4 1.001591 mfccfiles_5 1.001591 mfccfiles_6 1.001591 mfccfiles_7 1.001591 mfccfiles_8 1.001591 mfccfiles_9 1.001591 mfccfiles_10 1.001591 mfccfiles_11 1.001591 mfccfiles_12 1.001591 mfccfiles_13 1.001591 pitchfiles 0.000000 inharmonicityfiles 1.001591 bestkeyfiles 1.001591 keyclarityfiles 1.001591 modalityfiles 1.001591 tonalcentroidfiles_1 1.001591 tonalcentroidfiles_2 1.001591 tonalcentroidfiles_3 1.001591 tonalcentroidfiles_4 1.001591 tonalcentroidfiles_5 1.001591 tonalcentroidfiles_6 1.001591 chromagramfiles_1 1.001591 chromagramfiles_2 1.001591 chromagramfiles_3 1.001591 chromagramfiles_4 1.001591 chromagramfiles_5 1.001591 chromagramfiles_6 1.001591 chromagramfiles_7 1.001591 chromagramfiles_8 1.001591 chromagramfiles_9 1.001591 chromagramfiles_10 1.001591 chromagramfiles_11 1.001591 chromagramfiles_12 1.001591 attackslopefiles 1.001591 attackleapfiles 1.001591 chosen 1.001591 dtype: float64
Borramos pitch y bitrate porque todos sus valores son 0.
In [11]:
for i in range(len(companies)):
df_n_ps_std[i] = df_n_ps_std[i].drop(columns="pitchfiles")
df_n_ps_std[i] = df_n_ps_std[i].drop(columns="bitratefiles")
In [12]:
df_n_ps_std[0].columns
Out[12]:
Index(['durationfiles', 'rmsfiles', 'rmsmedianfiles', 'lowenergyfiles',
'ASRfiles', 'beatspectrumfiles', 'eventdensityfiles', 'tempofiles',
'pulseclarityfiles', 'zerocrossfiles', 'rolloffsfiles',
'brightnessfiles', 'spreadfiles', 'centroidfiles', 'kurtosisfiles',
'flatnessfiles', 'entropyfiles', 'mfccfiles_1', 'mfccfiles_2',
'mfccfiles_3', 'mfccfiles_4', 'mfccfiles_5', 'mfccfiles_6',
'mfccfiles_7', 'mfccfiles_8', 'mfccfiles_9', 'mfccfiles_10',
'mfccfiles_11', 'mfccfiles_12', 'mfccfiles_13', 'inharmonicityfiles',
'bestkeyfiles', 'keyclarityfiles', 'modalityfiles',
'tonalcentroidfiles_1', 'tonalcentroidfiles_2', 'tonalcentroidfiles_3',
'tonalcentroidfiles_4', 'tonalcentroidfiles_5', 'tonalcentroidfiles_6',
'chromagramfiles_1', 'chromagramfiles_2', 'chromagramfiles_3',
'chromagramfiles_4', 'chromagramfiles_5', 'chromagramfiles_6',
'chromagramfiles_7', 'chromagramfiles_8', 'chromagramfiles_9',
'chromagramfiles_10', 'chromagramfiles_11', 'chromagramfiles_12',
'attackslopefiles', 'attackleapfiles', 'chosen'],
dtype='object')
MFCC¶
In [71]:
df_n_ps_std[0].columns[17:30]
Out[71]:
Index(['mfccfiles_1', 'mfccfiles_2', 'mfccfiles_3', 'mfccfiles_4',
'mfccfiles_5', 'mfccfiles_6', 'mfccfiles_7', 'mfccfiles_8',
'mfccfiles_9', 'mfccfiles_10', 'mfccfiles_11', 'mfccfiles_12',
'mfccfiles_13'],
dtype='object')
In [72]:
df_n_ps_std_mfcc = [None]*len(companies)
for i in range(len(companies)):
df_n_ps_std_mfcc[i] = pd.DataFrame(df_n_ps_std[i].iloc[:,17:30])
df_n_ps_std_mfcc[i].columns=df_n_ps_std[i].columns[17:30]
df_n_ps_std_mfcc[0].info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 315 entries, 0 to 314 Data columns (total 13 columns): mfccfiles_1 315 non-null float64 mfccfiles_2 315 non-null float64 mfccfiles_3 315 non-null float64 mfccfiles_4 315 non-null float64 mfccfiles_5 315 non-null float64 mfccfiles_6 315 non-null float64 mfccfiles_7 315 non-null float64 mfccfiles_8 315 non-null float64 mfccfiles_9 315 non-null float64 mfccfiles_10 315 non-null float64 mfccfiles_11 315 non-null float64 mfccfiles_12 315 non-null float64 mfccfiles_13 315 non-null float64 dtypes: float64(13) memory usage: 32.1 KB
Arte Francés¶
ANN¶
In [23]:
import keras
keras.__version__
Using TensorFlow backend.
Out[23]:
'2.3.0'
In [24]:
from keras.layers import Input, Flatten, Dense#, Lambda
from keras.models import Model
from keras import layers
from keras import models, optimizers
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV #permite buscar la mejor configuración de parámetros con C-V
from sklearn.metrics import make_scorer # permite crear una clase scorer a partir de una función de score (necesario para el kappa)
from sklearn.metrics import accuracy_score, cohen_kappa_score, classification_report, roc_auc_score
from sklearn.model_selection import train_test_split #metodo de particionamiento de datasets para evaluación
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
In [108]:
X = df_n_ps_std_mfcc[0]
In [109]:
y = df_n_ps[0]['chosen']
In [110]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [111]:
X_train.shape
Out[111]:
(236, 14)
In [382]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [383]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [384]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [57]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'logistic', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.003, 'max_iter': 2000}, que permiten obtener un Accuracy de 81.78% y un Kappa del 50.99
Tiempo total: 61.22 minutos
In [31]:
grid.best_params_={'activation': 'sigmoid', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.003, 'max_iter': 2000}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [32]:
input_tensor = Input(shape = (n0,))
In [33]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [34]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [35]:
model.summary()
Model: "model_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_2 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_2 (Dense) (None, 30) 420 _________________________________________________________________ dense_3 (Dense) (None, 1) 31 ================================================================= Total params: 451 Trainable params: 451 Non-trainable params: 0 _________________________________________________________________
In [36]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 236 samples, validate on 79 samples Epoch 1/2000 236/236 [==============================] - 4s 17ms/step - loss: 0.8403 - accuracy: 0.3220 - val_loss: 0.7849 - val_accuracy: 0.2911 Epoch 2/2000 236/236 [==============================] - 0s 89us/step - loss: 0.7231 - accuracy: 0.4110 - val_loss: 0.6686 - val_accuracy: 0.5823 Epoch 3/2000 236/236 [==============================] - 0s 97us/step - loss: 0.6456 - accuracy: 0.6992 - val_loss: 0.5972 - val_accuracy: 0.7722 Epoch 4/2000 236/236 [==============================] - 0s 97us/step - loss: 0.6058 - accuracy: 0.6992 - val_loss: 0.5565 - val_accuracy: 0.7722 Epoch 5/2000 236/236 [==============================] - 0s 89us/step - loss: 0.5886 - accuracy: 0.7076 - val_loss: 0.5358 - val_accuracy: 0.7722 Epoch 6/2000 236/236 [==============================] - 0s 89us/step - loss: 0.5802 - accuracy: 0.7076 - val_loss: 0.5249 - val_accuracy: 0.7722 Epoch 7/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5748 - accuracy: 0.7076 - val_loss: 0.5191 - val_accuracy: 0.7722 Epoch 8/2000 236/236 [==============================] - 0s 85us/step - loss: 0.5701 - accuracy: 0.7076 - val_loss: 0.5152 - val_accuracy: 0.7722 Epoch 9/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5664 - accuracy: 0.7076 - val_loss: 0.5126 - val_accuracy: 0.7722 Epoch 10/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5613 - accuracy: 0.7119 - val_loss: 0.5099 - val_accuracy: 0.7848 Epoch 11/2000 236/236 [==============================] - 0s 89us/step - loss: 0.5575 - accuracy: 0.7119 - val_loss: 0.5071 - val_accuracy: 0.7848 Epoch 12/2000 236/236 [==============================] - 0s 80us/step - loss: 0.5544 - accuracy: 0.7119 - val_loss: 0.5053 - val_accuracy: 0.7848 Epoch 13/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5521 - accuracy: 0.7203 - val_loss: 0.5028 - val_accuracy: 0.7848 Epoch 14/2000 236/236 [==============================] - 0s 85us/step - loss: 0.5498 - accuracy: 0.7203 - val_loss: 0.4982 - val_accuracy: 0.7848 Epoch 15/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5466 - accuracy: 0.7161 - val_loss: 0.4979 - val_accuracy: 0.7848 Epoch 16/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5445 - accuracy: 0.7246 - val_loss: 0.4958 - val_accuracy: 0.7722 Epoch 17/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5428 - accuracy: 0.7288 - val_loss: 0.4940 - val_accuracy: 0.7722 Epoch 18/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5406 - accuracy: 0.7246 - val_loss: 0.4910 - val_accuracy: 0.7595 Epoch 19/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5381 - accuracy: 0.7288 - val_loss: 0.4885 - val_accuracy: 0.7595 Epoch 20/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5369 - accuracy: 0.7331 - val_loss: 0.4861 - val_accuracy: 0.7722 Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 21/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5354 - accuracy: 0.7331 - val_loss: 0.4850 - val_accuracy: 0.7722 Epoch 22/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5346 - accuracy: 0.7331 - val_loss: 0.4830 - val_accuracy: 0.7722 Epoch 23/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5346 - accuracy: 0.7288 - val_loss: 0.4812 - val_accuracy: 0.7722 Epoch 24/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5335 - accuracy: 0.7288 - val_loss: 0.4809 - val_accuracy: 0.7595 Epoch 25/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5323 - accuracy: 0.7288 - val_loss: 0.4811 - val_accuracy: 0.7595 Epoch 26/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5317 - accuracy: 0.7373 - val_loss: 0.4806 - val_accuracy: 0.7595 Epoch 27/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5314 - accuracy: 0.7331 - val_loss: 0.4805 - val_accuracy: 0.7595 Epoch 28/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5303 - accuracy: 0.7331 - val_loss: 0.4800 - val_accuracy: 0.7595 Epoch 29/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5299 - accuracy: 0.7458 - val_loss: 0.4789 - val_accuracy: 0.7595 Epoch 30/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5293 - accuracy: 0.7500 - val_loss: 0.4795 - val_accuracy: 0.7595 Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 31/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5288 - accuracy: 0.7500 - val_loss: 0.4794 - val_accuracy: 0.7595 Epoch 32/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5283 - accuracy: 0.7500 - val_loss: 0.4787 - val_accuracy: 0.7595 Epoch 33/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5282 - accuracy: 0.7500 - val_loss: 0.4780 - val_accuracy: 0.7595 Epoch 34/2000 236/236 [==============================] - 0s 157us/step - loss: 0.5278 - accuracy: 0.7500 - val_loss: 0.4773 - val_accuracy: 0.7595 Epoch 35/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5276 - accuracy: 0.7500 - val_loss: 0.4765 - val_accuracy: 0.7595 Epoch 36/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5274 - accuracy: 0.7500 - val_loss: 0.4759 - val_accuracy: 0.7595 Epoch 37/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5271 - accuracy: 0.7500 - val_loss: 0.4756 - val_accuracy: 0.7595 Epoch 38/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5269 - accuracy: 0.7500 - val_loss: 0.4753 - val_accuracy: 0.7595 Epoch 39/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5268 - accuracy: 0.7542 - val_loss: 0.4757 - val_accuracy: 0.7595 Epoch 40/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5265 - accuracy: 0.7542 - val_loss: 0.4755 - val_accuracy: 0.7595 Epoch 00040: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 41/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5263 - accuracy: 0.7542 - val_loss: 0.4755 - val_accuracy: 0.7468 Epoch 42/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5261 - accuracy: 0.7500 - val_loss: 0.4755 - val_accuracy: 0.7468 Epoch 43/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5260 - accuracy: 0.7500 - val_loss: 0.4755 - val_accuracy: 0.7468 Epoch 44/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5259 - accuracy: 0.7500 - val_loss: 0.4753 - val_accuracy: 0.7468 Epoch 45/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5258 - accuracy: 0.7500 - val_loss: 0.4751 - val_accuracy: 0.7468 Epoch 46/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5256 - accuracy: 0.7500 - val_loss: 0.4749 - val_accuracy: 0.7468 Epoch 47/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5255 - accuracy: 0.7500 - val_loss: 0.4743 - val_accuracy: 0.7468 Epoch 48/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5254 - accuracy: 0.7500 - val_loss: 0.4740 - val_accuracy: 0.7468 Epoch 49/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5253 - accuracy: 0.7542 - val_loss: 0.4737 - val_accuracy: 0.7468 Epoch 50/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5252 - accuracy: 0.7542 - val_loss: 0.4735 - val_accuracy: 0.7468 Epoch 00050: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 51/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5250 - accuracy: 0.7542 - val_loss: 0.4734 - val_accuracy: 0.7468 Epoch 52/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5250 - accuracy: 0.7542 - val_loss: 0.4732 - val_accuracy: 0.7468 Epoch 53/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5249 - accuracy: 0.7542 - val_loss: 0.4731 - val_accuracy: 0.7468 Epoch 54/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5248 - accuracy: 0.7542 - val_loss: 0.4730 - val_accuracy: 0.7468 Epoch 55/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5248 - accuracy: 0.7542 - val_loss: 0.4729 - val_accuracy: 0.7468 Epoch 56/2000 236/236 [==============================] - 0s 157us/step - loss: 0.5247 - accuracy: 0.7542 - val_loss: 0.4728 - val_accuracy: 0.7468 Epoch 57/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5247 - accuracy: 0.7500 - val_loss: 0.4728 - val_accuracy: 0.7468 Epoch 58/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5246 - accuracy: 0.7500 - val_loss: 0.4727 - val_accuracy: 0.7468 Epoch 59/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5245 - accuracy: 0.7500 - val_loss: 0.4726 - val_accuracy: 0.7468 Epoch 60/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5245 - accuracy: 0.7500 - val_loss: 0.4724 - val_accuracy: 0.7468 Epoch 00060: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 61/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5244 - accuracy: 0.7500 - val_loss: 0.4723 - val_accuracy: 0.7468 Epoch 62/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5244 - accuracy: 0.7500 - val_loss: 0.4722 - val_accuracy: 0.7468 Epoch 63/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5244 - accuracy: 0.7500 - val_loss: 0.4722 - val_accuracy: 0.7468 Epoch 64/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5244 - accuracy: 0.7500 - val_loss: 0.4721 - val_accuracy: 0.7468 Epoch 65/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5243 - accuracy: 0.7500 - val_loss: 0.4721 - val_accuracy: 0.7468 Epoch 66/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5243 - accuracy: 0.7500 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 67/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5243 - accuracy: 0.7500 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 68/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5242 - accuracy: 0.7458 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 69/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5242 - accuracy: 0.7458 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 70/2000 236/236 [==============================] - 0s 161us/step - loss: 0.5242 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 00070: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 71/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5242 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 72/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5241 - accuracy: 0.7458 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 73/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5241 - accuracy: 0.7458 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 74/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5241 - accuracy: 0.7458 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 75/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5241 - accuracy: 0.7458 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 76/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5241 - accuracy: 0.7458 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 77/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5241 - accuracy: 0.7458 - val_loss: 0.4720 - val_accuracy: 0.7468 Epoch 78/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5241 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 79/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 80/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 00080: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 81/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 82/2000 236/236 [==============================] - 0s 152us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 83/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 84/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 85/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 86/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 87/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 88/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 89/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5240 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 90/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00090: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 91/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 92/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 93/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 94/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 95/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4719 - val_accuracy: 0.7468 Epoch 96/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 97/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 98/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 99/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 100/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00100: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 101/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 102/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 103/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 104/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 105/2000 236/236 [==============================] - 0s 161us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 106/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 107/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 108/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 109/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 110/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00110: ReduceLROnPlateau reducing learning rate to 2.9296875254658516e-06. Epoch 111/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 112/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 113/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 114/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 115/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 116/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 117/2000 236/236 [==============================] - ETA: 0s - loss: 0.4906 - accuracy: 0.81 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 118/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 119/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 120/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00120: ReduceLROnPlateau reducing learning rate to 1.4648437627329258e-06. Epoch 121/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 122/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 123/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 124/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 125/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 126/2000 236/236 [==============================] - 0s 191us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 127/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 128/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 129/2000 236/236 [==============================] - 0s 165us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 130/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00130: ReduceLROnPlateau reducing learning rate to 7.324218813664629e-07. Epoch 131/2000 236/236 [==============================] - 0s 203us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 132/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 133/2000 236/236 [==============================] - 0s 157us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 134/2000 236/236 [==============================] - 0s 89us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 135/2000 236/236 [==============================] - 0s 220us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 136/2000 236/236 [==============================] - 0s 178us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 137/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 138/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 139/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 140/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00140: ReduceLROnPlateau reducing learning rate to 3.6621094068323146e-07. Epoch 141/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 142/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 143/2000 236/236 [==============================] - 0s 186us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 144/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 145/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 146/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 147/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 148/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 149/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 150/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00150: ReduceLROnPlateau reducing learning rate to 1.8310547034161573e-07. Epoch 151/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 152/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 153/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 154/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 155/2000 236/236 [==============================] - ETA: 0s - loss: 0.4318 - accuracy: 0.84 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 156/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 157/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 158/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 159/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 160/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00160: ReduceLROnPlateau reducing learning rate to 9.155273517080786e-08. Epoch 161/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 162/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 163/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 164/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 165/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 166/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 167/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 168/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 169/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 170/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00170: ReduceLROnPlateau reducing learning rate to 4.577636758540393e-08. Epoch 171/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 172/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 173/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 174/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 175/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 176/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 177/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 178/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 179/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 180/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00180: ReduceLROnPlateau reducing learning rate to 2.2888183792701966e-08. Epoch 181/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 182/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 183/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 184/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 185/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 186/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 187/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 188/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 189/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 190/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00190: ReduceLROnPlateau reducing learning rate to 1.1444091896350983e-08. Epoch 191/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 192/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 193/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 194/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 195/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 196/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 197/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 198/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 199/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 200/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00200: ReduceLROnPlateau reducing learning rate to 5.7220459481754915e-09. Epoch 201/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 202/2000 236/236 [==============================] - ETA: 0s - loss: 0.6799 - accuracy: 0.62 - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 203/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 204/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 205/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 206/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 207/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 208/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 209/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 210/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00210: ReduceLROnPlateau reducing learning rate to 2.8610229740877458e-09. Epoch 211/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 212/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 213/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 214/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 215/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 216/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 217/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 218/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 219/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 220/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00220: ReduceLROnPlateau reducing learning rate to 1.4305114870438729e-09. Epoch 221/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 222/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 223/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 224/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 225/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 226/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 227/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 228/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 229/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 230/2000 236/236 [==============================] - ETA: 0s - loss: 0.4254 - accuracy: 0.81 - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00230: ReduceLROnPlateau reducing learning rate to 7.152557435219364e-10. Epoch 231/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 232/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 233/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 234/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 235/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 236/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 237/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 238/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 239/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 240/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00240: ReduceLROnPlateau reducing learning rate to 3.576278717609682e-10. Epoch 241/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 242/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 243/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 244/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 245/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 246/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 247/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 248/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 249/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 250/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00250: ReduceLROnPlateau reducing learning rate to 1.788139358804841e-10. Epoch 251/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 252/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 253/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 254/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 255/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 256/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 257/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 258/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 259/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 260/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00260: ReduceLROnPlateau reducing learning rate to 8.940696794024205e-11. Epoch 261/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 262/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 263/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 264/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 265/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 266/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 267/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 268/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 269/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 270/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00270: ReduceLROnPlateau reducing learning rate to 4.470348397012103e-11. Epoch 271/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 272/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 273/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 274/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 275/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 276/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 277/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 278/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 279/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 280/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00280: ReduceLROnPlateau reducing learning rate to 2.2351741985060514e-11. Epoch 281/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 282/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 283/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 284/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 285/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 286/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 287/2000 236/236 [==============================] - 0s 85us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 288/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 289/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 290/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00290: ReduceLROnPlateau reducing learning rate to 1.1175870992530257e-11. Epoch 291/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 292/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 293/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 294/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 295/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 296/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 297/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 298/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 299/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 300/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00300: ReduceLROnPlateau reducing learning rate to 5.5879354962651284e-12. Epoch 301/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 302/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 303/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 304/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 305/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 306/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 307/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 308/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 309/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 310/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00310: ReduceLROnPlateau reducing learning rate to 2.7939677481325642e-12. Epoch 311/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 312/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 313/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 314/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 315/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 316/2000 236/236 [==============================] - ETA: 0s - loss: 0.5187 - accuracy: 0.75 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 317/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 318/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 319/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 320/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00320: ReduceLROnPlateau reducing learning rate to 1.3969838740662821e-12. Epoch 321/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 322/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 323/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 324/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 325/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 326/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 327/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 328/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 329/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 330/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00330: ReduceLROnPlateau reducing learning rate to 6.984919370331411e-13. Epoch 331/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 332/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 333/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 334/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 335/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 336/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 337/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 338/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 339/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 340/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00340: ReduceLROnPlateau reducing learning rate to 3.4924596851657053e-13. Epoch 341/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 342/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 343/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 344/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 345/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 346/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 347/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 348/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 349/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 350/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00350: ReduceLROnPlateau reducing learning rate to 1.7462298425828526e-13. Epoch 351/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 352/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 353/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 354/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 355/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 356/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 357/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 358/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 359/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 360/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00360: ReduceLROnPlateau reducing learning rate to 8.731149212914263e-14. Epoch 361/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 362/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 363/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 364/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 365/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 366/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 367/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 368/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 369/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 370/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00370: ReduceLROnPlateau reducing learning rate to 4.3655746064571316e-14. Epoch 371/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 372/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 373/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 374/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 375/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 376/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 377/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 378/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 379/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 380/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00380: ReduceLROnPlateau reducing learning rate to 2.1827873032285658e-14. Epoch 381/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 382/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 383/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 384/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 385/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 386/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 387/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 388/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 389/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 390/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00390: ReduceLROnPlateau reducing learning rate to 1.0913936516142829e-14. Epoch 391/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 392/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 393/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 394/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 395/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 396/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 397/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 398/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 399/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 400/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00400: ReduceLROnPlateau reducing learning rate to 5.4569682580714145e-15. Epoch 401/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 402/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 403/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 404/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 405/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 406/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 407/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 408/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 409/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 410/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00410: ReduceLROnPlateau reducing learning rate to 2.7284841290357072e-15. Epoch 411/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 412/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 413/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 414/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 415/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 416/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 417/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 418/2000 236/236 [==============================] - ETA: 0s - loss: 0.4688 - accuracy: 0.81 - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 419/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 420/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00420: ReduceLROnPlateau reducing learning rate to 1.3642420645178536e-15. Epoch 421/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 422/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 423/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 424/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 425/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 426/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 427/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 428/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 429/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 430/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00430: ReduceLROnPlateau reducing learning rate to 6.821210322589268e-16. Epoch 431/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 432/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 433/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 434/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 435/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 436/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 437/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 438/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 439/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 440/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00440: ReduceLROnPlateau reducing learning rate to 3.410605161294634e-16. Epoch 441/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 442/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 443/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 444/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 445/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 446/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 447/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 448/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 449/2000 236/236 [==============================] - ETA: 0s - loss: 0.5581 - accuracy: 0.71 - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 450/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00450: ReduceLROnPlateau reducing learning rate to 1.705302580647317e-16. Epoch 451/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 452/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 453/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 454/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 455/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 456/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 457/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 458/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 459/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 460/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00460: ReduceLROnPlateau reducing learning rate to 8.526512903236585e-17. Epoch 461/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 462/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 463/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 464/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 465/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 466/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 467/2000 236/236 [==============================] - 0s 220us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 468/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 469/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 470/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00470: ReduceLROnPlateau reducing learning rate to 4.2632564516182926e-17. Epoch 471/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 472/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 473/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 474/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 475/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 476/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 477/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 478/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 479/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 480/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00480: ReduceLROnPlateau reducing learning rate to 2.1316282258091463e-17. Epoch 481/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 482/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 483/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 484/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 485/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 486/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 487/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 488/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 489/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 490/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00490: ReduceLROnPlateau reducing learning rate to 1.0658141129045731e-17. Epoch 491/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 492/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 493/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 494/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 495/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 496/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 497/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 498/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 499/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 500/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00500: ReduceLROnPlateau reducing learning rate to 5.329070564522866e-18. Epoch 501/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 502/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 503/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 504/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 505/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 506/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 507/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 508/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 509/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 510/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00510: ReduceLROnPlateau reducing learning rate to 2.664535282261433e-18. Epoch 511/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 512/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 513/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 514/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 515/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 516/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 517/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 518/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 519/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 520/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00520: ReduceLROnPlateau reducing learning rate to 1.3322676411307164e-18. Epoch 521/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 522/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 523/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 524/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 525/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 526/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 527/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 528/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 529/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 530/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00530: ReduceLROnPlateau reducing learning rate to 6.661338205653582e-19. Epoch 531/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 532/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 533/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 534/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 535/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 536/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 537/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 538/2000 236/236 [==============================] - 0s 165us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 539/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 540/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00540: ReduceLROnPlateau reducing learning rate to 3.330669102826791e-19. Epoch 541/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 542/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 543/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 544/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 545/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 546/2000 236/236 [==============================] - 0s 220us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 547/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 548/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 549/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 550/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00550: ReduceLROnPlateau reducing learning rate to 1.6653345514133955e-19. Epoch 551/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 552/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 553/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 554/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 555/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 556/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 557/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 558/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 559/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 560/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00560: ReduceLROnPlateau reducing learning rate to 8.326672757066978e-20. Epoch 561/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 562/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 563/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 564/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 565/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 566/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 567/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 568/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 569/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 570/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00570: ReduceLROnPlateau reducing learning rate to 4.163336378533489e-20. Epoch 571/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 572/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 573/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 574/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 575/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 576/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 577/2000 236/236 [==============================] - ETA: 0s - loss: 0.6339 - accuracy: 0.59 - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 578/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 579/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 580/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00580: ReduceLROnPlateau reducing learning rate to 2.0816681892667444e-20. Epoch 581/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 582/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 583/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 584/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 585/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 586/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 587/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 588/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 589/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 590/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00590: ReduceLROnPlateau reducing learning rate to 1.0408340946333722e-20. Epoch 591/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 592/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 593/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 594/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 595/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 596/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 597/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 598/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 599/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 600/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00600: ReduceLROnPlateau reducing learning rate to 5.204170473166861e-21. Epoch 601/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 602/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 603/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 604/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 605/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 606/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 607/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 608/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 609/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 610/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00610: ReduceLROnPlateau reducing learning rate to 2.6020852365834305e-21. Epoch 611/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 612/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 613/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 614/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 615/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 616/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 617/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 618/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 619/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 620/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00620: ReduceLROnPlateau reducing learning rate to 1.3010426182917153e-21. Epoch 621/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 622/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 623/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 624/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 625/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 626/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 627/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 628/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 629/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 630/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00630: ReduceLROnPlateau reducing learning rate to 6.505213091458576e-22. Epoch 631/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 632/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 633/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 634/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 635/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 636/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 637/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 638/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 639/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 640/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00640: ReduceLROnPlateau reducing learning rate to 3.252606545729288e-22. Epoch 641/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 642/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 643/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 644/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 645/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 646/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 647/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 648/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 649/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 650/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00650: ReduceLROnPlateau reducing learning rate to 1.626303272864644e-22. Epoch 651/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 652/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 653/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 654/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 655/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 656/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 657/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 658/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 659/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 660/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00660: ReduceLROnPlateau reducing learning rate to 8.13151636432322e-23. Epoch 661/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 662/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 663/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 664/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 665/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 666/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 667/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 668/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 669/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 670/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00670: ReduceLROnPlateau reducing learning rate to 4.06575818216161e-23. Epoch 671/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 672/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 673/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 674/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 675/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 676/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 677/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 678/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 679/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 680/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00680: ReduceLROnPlateau reducing learning rate to 2.032879091080805e-23. Epoch 681/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 682/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 683/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 684/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 685/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 686/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 687/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 688/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 689/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 690/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00690: ReduceLROnPlateau reducing learning rate to 1.0164395455404025e-23. Epoch 691/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 692/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 693/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 694/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 695/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 696/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 697/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 698/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 699/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 700/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00700: ReduceLROnPlateau reducing learning rate to 5.082197727702013e-24. Epoch 701/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 702/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 703/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 704/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 705/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 706/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 707/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 708/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 709/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 710/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00710: ReduceLROnPlateau reducing learning rate to 2.5410988638510064e-24. Epoch 711/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 712/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 713/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 714/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 715/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 716/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 717/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 718/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 719/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 720/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00720: ReduceLROnPlateau reducing learning rate to 1.2705494319255032e-24. Epoch 721/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 722/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 723/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 724/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 725/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 726/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 727/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 728/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 729/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 730/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00730: ReduceLROnPlateau reducing learning rate to 6.352747159627516e-25. Epoch 731/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 732/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 733/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 734/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 735/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 736/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 737/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 738/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 739/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 740/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00740: ReduceLROnPlateau reducing learning rate to 3.176373579813758e-25. Epoch 741/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 742/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 743/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 744/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 745/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 746/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 747/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 748/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 749/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 750/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00750: ReduceLROnPlateau reducing learning rate to 1.588186789906879e-25. Epoch 751/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 752/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 753/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 754/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 755/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 756/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 757/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 758/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 759/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 760/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00760: ReduceLROnPlateau reducing learning rate to 7.940933949534395e-26. Epoch 761/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 762/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 763/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 764/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 765/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 766/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 767/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 768/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 769/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 770/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00770: ReduceLROnPlateau reducing learning rate to 3.9704669747671974e-26. Epoch 771/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 772/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 773/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 774/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 775/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 776/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 777/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 778/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 779/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 780/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00780: ReduceLROnPlateau reducing learning rate to 1.9852334873835987e-26. Epoch 781/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 782/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 783/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 784/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 785/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 786/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 787/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 788/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 789/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 790/2000 236/236 [==============================] - ETA: 0s - loss: 0.5123 - accuracy: 0.81 - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00790: ReduceLROnPlateau reducing learning rate to 9.926167436917994e-27. Epoch 791/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 792/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 793/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 794/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 795/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 796/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 797/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 798/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 799/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 800/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00800: ReduceLROnPlateau reducing learning rate to 4.963083718458997e-27. Epoch 801/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 802/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 803/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 804/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 805/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 806/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 807/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 808/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 809/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 810/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00810: ReduceLROnPlateau reducing learning rate to 2.4815418592294984e-27. Epoch 811/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 812/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 813/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 814/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 815/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 816/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 817/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 818/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 819/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 820/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00820: ReduceLROnPlateau reducing learning rate to 1.2407709296147492e-27. Epoch 821/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 822/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 823/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 824/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 825/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 826/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 827/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 828/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 829/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 830/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00830: ReduceLROnPlateau reducing learning rate to 6.203854648073746e-28. Epoch 831/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 832/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 833/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 834/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 835/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 836/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 837/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 838/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 839/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 840/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00840: ReduceLROnPlateau reducing learning rate to 3.101927324036873e-28. Epoch 841/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 842/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 843/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 844/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 845/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 846/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 847/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 848/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 849/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 850/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00850: ReduceLROnPlateau reducing learning rate to 1.5509636620184365e-28. Epoch 851/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 852/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 853/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 854/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 855/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 856/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 857/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 858/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 859/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 860/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00860: ReduceLROnPlateau reducing learning rate to 7.754818310092183e-29. Epoch 861/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 862/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 863/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 864/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 865/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 866/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 867/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 868/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 869/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 870/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00870: ReduceLROnPlateau reducing learning rate to 3.877409155046091e-29. Epoch 871/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 872/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 873/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 874/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 875/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 876/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 877/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 878/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 879/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 880/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00880: ReduceLROnPlateau reducing learning rate to 1.9387045775230456e-29. Epoch 881/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 882/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 883/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 884/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 885/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 886/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 887/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 888/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 889/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 890/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00890: ReduceLROnPlateau reducing learning rate to 9.693522887615228e-30. Epoch 891/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 892/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 893/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 894/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 895/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 896/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 897/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 898/2000 236/236 [==============================] - ETA: 0s - loss: 0.5093 - accuracy: 0.75 - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 899/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 900/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00900: ReduceLROnPlateau reducing learning rate to 4.846761443807614e-30. Epoch 901/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 902/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 903/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 904/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 905/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 906/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 907/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 908/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 909/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 910/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00910: ReduceLROnPlateau reducing learning rate to 2.423380721903807e-30. Epoch 911/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 912/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 913/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 914/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 915/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 916/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 917/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 918/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 919/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 920/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00920: ReduceLROnPlateau reducing learning rate to 1.2116903609519035e-30. Epoch 921/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 922/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 923/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 924/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 925/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 926/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 927/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 928/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 929/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 930/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00930: ReduceLROnPlateau reducing learning rate to 6.058451804759518e-31. Epoch 931/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 932/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 933/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 934/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 935/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 936/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 937/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 938/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 939/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 940/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00940: ReduceLROnPlateau reducing learning rate to 3.029225902379759e-31. Epoch 941/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 942/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 943/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 944/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 945/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 946/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 947/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 948/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 949/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 950/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00950: ReduceLROnPlateau reducing learning rate to 1.5146129511898794e-31. Epoch 951/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 952/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 953/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 954/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 955/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 956/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 957/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 958/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 959/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 960/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00960: ReduceLROnPlateau reducing learning rate to 7.573064755949397e-32. Epoch 961/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 962/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 963/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 964/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 965/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 966/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 967/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 968/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 969/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 970/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00970: ReduceLROnPlateau reducing learning rate to 3.7865323779746985e-32. Epoch 971/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 972/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 973/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 974/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 975/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 976/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 977/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 978/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 979/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 980/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00980: ReduceLROnPlateau reducing learning rate to 1.8932661889873492e-32. Epoch 981/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 982/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 983/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 984/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 985/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 986/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 987/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 988/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 989/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 990/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 00990: ReduceLROnPlateau reducing learning rate to 9.466330944936746e-33. Epoch 991/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 992/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 993/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 994/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 995/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 996/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 997/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 998/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 999/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1000/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01000: ReduceLROnPlateau reducing learning rate to 4.733165472468373e-33. Epoch 1001/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1002/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1003/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1004/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1005/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1006/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1007/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1008/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1009/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1010/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01010: ReduceLROnPlateau reducing learning rate to 2.3665827362341866e-33. Epoch 1011/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1012/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1013/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1014/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1015/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1016/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1017/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1018/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1019/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1020/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01020: ReduceLROnPlateau reducing learning rate to 1.1832913681170933e-33. Epoch 1021/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1022/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1023/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1024/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1025/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1026/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1027/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1028/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1029/2000 236/236 [==============================] - 0s 178us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1030/2000 236/236 [==============================] - 0s 271us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01030: ReduceLROnPlateau reducing learning rate to 5.916456840585466e-34. Epoch 1031/2000 236/236 [==============================] - 0s 246us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1032/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1033/2000 236/236 [==============================] - 0s 237us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1034/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1035/2000 236/236 [==============================] - 0s 182us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1036/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1037/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1038/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1039/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1040/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01040: ReduceLROnPlateau reducing learning rate to 2.958228420292733e-34. Epoch 1041/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1042/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1043/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1044/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1045/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1046/2000 236/236 [==============================] - ETA: 0s - loss: 0.5555 - accuracy: 0.71 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1047/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1048/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1049/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1050/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01050: ReduceLROnPlateau reducing learning rate to 1.4791142101463666e-34. Epoch 1051/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1052/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1053/2000 236/236 [==============================] - 0s 186us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1054/2000 236/236 [==============================] - 0s 178us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1055/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1056/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1057/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1058/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1059/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1060/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01060: ReduceLROnPlateau reducing learning rate to 7.395571050731833e-35. Epoch 1061/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1062/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1063/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1064/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1065/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1066/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1067/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1068/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1069/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1070/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01070: ReduceLROnPlateau reducing learning rate to 3.6977855253659165e-35. Epoch 1071/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1072/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1073/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1074/2000 236/236 [==============================] - ETA: 0s - loss: 0.5019 - accuracy: 0.75 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1075/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1076/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1077/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1078/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1079/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1080/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01080: ReduceLROnPlateau reducing learning rate to 1.8488927626829582e-35. Epoch 1081/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1082/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1083/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1084/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1085/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1086/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1087/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1088/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1089/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1090/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01090: ReduceLROnPlateau reducing learning rate to 9.244463813414791e-36. Epoch 1091/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1092/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1093/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1094/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1095/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1096/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1097/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1098/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1099/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1100/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01100: ReduceLROnPlateau reducing learning rate to 4.6222319067073956e-36. Epoch 1101/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1102/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1103/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1104/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1105/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1106/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1107/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1108/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1109/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1110/2000 236/236 [==============================] - ETA: 0s - loss: 0.4339 - accuracy: 0.84 - 0s 157us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01110: ReduceLROnPlateau reducing learning rate to 2.3111159533536978e-36. Epoch 1111/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1112/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1113/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1114/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1115/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1116/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1117/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1118/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1119/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1120/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01120: ReduceLROnPlateau reducing learning rate to 1.1555579766768489e-36. Epoch 1121/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1122/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1123/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1124/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1125/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1126/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1127/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1128/2000 236/236 [==============================] - 0s 250us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1129/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1130/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01130: ReduceLROnPlateau reducing learning rate to 5.7777898833842445e-37. Epoch 1131/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1132/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1133/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1134/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1135/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1136/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1137/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1138/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1139/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1140/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01140: ReduceLROnPlateau reducing learning rate to 2.8888949416921223e-37. Epoch 1141/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1142/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1143/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1144/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1145/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1146/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1147/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1148/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1149/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1150/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01150: ReduceLROnPlateau reducing learning rate to 1.4444474708460611e-37. Epoch 1151/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1152/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1153/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1154/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1155/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1156/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1157/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1158/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1159/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1160/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01160: ReduceLROnPlateau reducing learning rate to 7.222237354230306e-38. Epoch 1161/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1162/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1163/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1164/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1165/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1166/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1167/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1168/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1169/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1170/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01170: ReduceLROnPlateau reducing learning rate to 3.611118677115153e-38. Epoch 1171/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1172/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1173/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1174/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1175/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1176/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1177/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1178/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1179/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1180/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01180: ReduceLROnPlateau reducing learning rate to 1.8055593385575764e-38. Epoch 1181/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1182/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1183/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1184/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1185/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1186/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1187/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1188/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1189/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1190/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01190: ReduceLROnPlateau reducing learning rate to 9.027796692787882e-39. Epoch 1191/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1192/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1193/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1194/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1195/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1196/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1197/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1198/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1199/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1200/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01200: ReduceLROnPlateau reducing learning rate to 4.513898346393941e-39. Epoch 1201/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1202/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1203/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1204/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1205/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1206/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1207/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1208/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1209/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1210/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01210: ReduceLROnPlateau reducing learning rate to 2.2569495235215866e-39. Epoch 1211/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1212/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1213/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1214/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1215/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1216/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1217/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1218/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1219/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1220/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01220: ReduceLROnPlateau reducing learning rate to 1.1284747617607933e-39. Epoch 1221/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1222/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1223/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1224/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1225/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1226/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1227/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1228/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1229/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1230/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01230: ReduceLROnPlateau reducing learning rate to 5.642370305557806e-40. Epoch 1231/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1232/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1233/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1234/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1235/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1236/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1237/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1238/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1239/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1240/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01240: ReduceLROnPlateau reducing learning rate to 2.821185152778903e-40. Epoch 1241/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1242/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1243/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1244/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1245/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1246/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1247/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1248/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1249/2000 236/236 [==============================] - ETA: 0s - loss: 0.6118 - accuracy: 0.53 - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1250/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01250: ReduceLROnPlateau reducing learning rate to 1.4105890731432906e-40. Epoch 1251/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1252/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1253/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1254/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1255/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1256/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1257/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1258/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1259/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1260/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01260: ReduceLROnPlateau reducing learning rate to 7.052945365716453e-41. Epoch 1261/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1262/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1263/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1264/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1265/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1266/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1267/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1268/2000 236/236 [==============================] - ETA: 0s - loss: 0.5074 - accuracy: 0.75 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1269/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1270/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01270: ReduceLROnPlateau reducing learning rate to 3.5265077153198346e-41. Epoch 1271/2000 236/236 [==============================] - 0s 174us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1272/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1273/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1274/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1275/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1276/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1277/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1278/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1279/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1280/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01280: ReduceLROnPlateau reducing learning rate to 1.7632538576599173e-41. Epoch 1281/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1282/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1283/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1284/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1285/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1286/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1287/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1288/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1289/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1290/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01290: ReduceLROnPlateau reducing learning rate to 8.816269288299587e-42. Epoch 1291/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1292/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1293/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1294/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1295/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1296/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1297/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1298/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1299/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1300/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01300: ReduceLROnPlateau reducing learning rate to 4.4084849687658745e-42. Epoch 1301/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1302/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1303/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1304/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1305/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1306/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1307/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1308/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1309/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1310/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01310: ReduceLROnPlateau reducing learning rate to 2.2042424843829373e-42. Epoch 1311/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1312/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1313/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1314/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1315/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1316/2000 236/236 [==============================] - 0s 161us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1317/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1318/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1319/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1320/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01320: ReduceLROnPlateau reducing learning rate to 1.1021212421914686e-42. Epoch 1321/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1322/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1323/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1324/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1325/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1326/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1327/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1328/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1329/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1330/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01330: ReduceLROnPlateau reducing learning rate to 5.507102964796531e-43. Epoch 1331/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1332/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1333/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1334/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1335/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1336/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1337/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1338/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1339/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1340/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01340: ReduceLROnPlateau reducing learning rate to 2.7535514823982655e-43. Epoch 1341/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1342/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1343/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1344/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1345/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1346/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1347/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1348/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1349/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1350/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01350: ReduceLROnPlateau reducing learning rate to 1.3732724950383207e-43. Epoch 1351/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1352/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1353/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1354/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1355/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1356/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1357/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1358/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1359/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1360/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01360: ReduceLROnPlateau reducing learning rate to 6.866362475191604e-44. Epoch 1361/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1362/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1363/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1364/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1365/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1366/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1367/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1368/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1369/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1370/2000 236/236 [==============================] - 0s 152us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01370: ReduceLROnPlateau reducing learning rate to 3.433181237595802e-44. Epoch 1371/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1372/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1373/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1374/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1375/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1376/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1377/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1378/2000 236/236 [==============================] - 0s 152us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1379/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1380/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01380: ReduceLROnPlateau reducing learning rate to 1.6815581571897805e-44. Epoch 1381/2000 236/236 [==============================] - 0s 165us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1382/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1383/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1384/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1385/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1386/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1387/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1388/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1389/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1390/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01390: ReduceLROnPlateau reducing learning rate to 8.407790785948902e-45. Epoch 1391/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1392/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1393/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1394/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1395/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1396/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1397/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1398/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1399/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1400/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01400: ReduceLROnPlateau reducing learning rate to 4.203895392974451e-45. Epoch 1401/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1402/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1403/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1404/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1405/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1406/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1407/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1408/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1409/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1410/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01410: ReduceLROnPlateau reducing learning rate to 2.1019476964872256e-45. Epoch 1411/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1412/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1413/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1414/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1415/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1416/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1417/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1418/2000 236/236 [==============================] - 0s 161us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1419/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1420/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01420: ReduceLROnPlateau reducing learning rate to 1.401298464324817e-45. Epoch 1421/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1422/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1423/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1424/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1425/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1426/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1427/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1428/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1429/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1430/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 01430: ReduceLROnPlateau reducing learning rate to 7.006492321624085e-46. Epoch 1431/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1432/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1433/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1434/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1435/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1436/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1437/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1438/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1439/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1440/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1441/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1442/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1443/2000 236/236 [==============================] - 0s 85us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1444/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1445/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1446/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1447/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1448/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1449/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1450/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1451/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1452/2000 236/236 [==============================] - 0s 174us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1453/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1454/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1455/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1456/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1457/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1458/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1459/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1460/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1461/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1462/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1463/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1464/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1465/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1466/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1467/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1468/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1469/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1470/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1471/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1472/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1473/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1474/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1475/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1476/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1477/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1478/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1479/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1480/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1481/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1482/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1483/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1484/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1485/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1486/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1487/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1488/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1489/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1490/2000 236/236 [==============================] - 0s 157us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1491/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1492/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1493/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1494/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1495/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1496/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1497/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1498/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1499/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1500/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1501/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1502/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1503/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1504/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1505/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1506/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1507/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1508/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1509/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1510/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1511/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1512/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1513/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1514/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1515/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1516/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1517/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1518/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1519/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1520/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1521/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1522/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1523/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1524/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1525/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1526/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1527/2000 236/236 [==============================] - 0s 258us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1528/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1529/2000 236/236 [==============================] - 0s 157us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1530/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1531/2000 236/236 [==============================] - 0s 157us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1532/2000 236/236 [==============================] - 0s 165us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1533/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1534/2000 236/236 [==============================] - 0s 169us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1535/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1536/2000 236/236 [==============================] - 0s 208us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1537/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1538/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1539/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1540/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1541/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1542/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1543/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1544/2000 236/236 [==============================] - 0s 254us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1545/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1546/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1547/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1548/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1549/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1550/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1551/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1552/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1553/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1554/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1555/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1556/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1557/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1558/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1559/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1560/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1561/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1562/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1563/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1564/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1565/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1566/2000 236/236 [==============================] - 0s 148us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1567/2000 236/236 [==============================] - 0s 212us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1568/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1569/2000 236/236 [==============================] - 0s 165us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1570/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1571/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1572/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1573/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1574/2000 236/236 [==============================] - 0s 144us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1575/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1576/2000 236/236 [==============================] - 0s 220us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1577/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1578/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1579/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1580/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1581/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1582/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1583/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1584/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1585/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1586/2000 236/236 [==============================] - 0s 186us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1587/2000 236/236 [==============================] - 0s 169us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1588/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1589/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1590/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1591/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1592/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1593/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1594/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1595/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1596/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1597/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1598/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1599/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1600/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1601/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1602/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1603/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1604/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1605/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1606/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1607/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1608/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1609/2000 236/236 [==============================] - 0s 186us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1610/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1611/2000 236/236 [==============================] - 0s 169us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1612/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1613/2000 236/236 [==============================] - 0s 140us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1614/2000 236/236 [==============================] - 0s 178us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1615/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1616/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1617/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1618/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1619/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1620/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1621/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1622/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1623/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1624/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1625/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1626/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1627/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1628/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1629/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1630/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1631/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1632/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1633/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1634/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1635/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1636/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1637/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1638/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1639/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1640/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1641/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1642/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1643/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1644/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1645/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1646/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1647/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1648/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1649/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1650/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1651/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1652/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1653/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1654/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1655/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1656/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1657/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1658/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1659/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1660/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1661/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1662/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1663/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1664/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1665/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1666/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1667/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1668/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1669/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1670/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1671/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1672/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1673/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1674/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1675/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1676/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1677/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1678/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1679/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1680/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1681/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1682/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1683/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1684/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1685/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1686/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1687/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1688/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1689/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1690/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1691/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1692/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1693/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1694/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1695/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1696/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1697/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1698/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1699/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1700/2000 236/236 [==============================] - ETA: 0s - loss: 0.4052 - accuracy: 0.87 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1701/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1702/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1703/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1704/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1705/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1706/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1707/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1708/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1709/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1710/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1711/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1712/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1713/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1714/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1715/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1716/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1717/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1718/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1719/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1720/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1721/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1722/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1723/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1724/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1725/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1726/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1727/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1728/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1729/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1730/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1731/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1732/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1733/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1734/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1735/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1736/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1737/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1738/2000 236/236 [==============================] - ETA: 0s - loss: 0.6498 - accuracy: 0.71 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1739/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1740/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1741/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1742/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1743/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1744/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1745/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1746/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1747/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1748/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1749/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1750/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1751/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1752/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1753/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1754/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1755/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1756/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1757/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1758/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1759/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1760/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1761/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1762/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1763/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1764/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1765/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1766/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1767/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1768/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1769/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1770/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1771/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1772/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1773/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1774/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1775/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1776/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1777/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1778/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1779/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1780/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1781/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1782/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1783/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1784/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1785/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1786/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1787/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1788/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1789/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1790/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1791/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1792/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1793/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1794/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1795/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1796/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1797/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1798/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1799/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1800/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1801/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1802/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1803/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1804/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1805/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1806/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1807/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1808/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1809/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1810/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1811/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1812/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1813/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1814/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1815/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1816/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1817/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1818/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1819/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1820/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1821/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1822/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1823/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1824/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1825/2000 236/236 [==============================] - 0s 131us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1826/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1827/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1828/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1829/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1830/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1831/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1832/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1833/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1834/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1835/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1836/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1837/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1838/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1839/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1840/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1841/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1842/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1843/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1844/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1845/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1846/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1847/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1848/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1849/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1850/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1851/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1852/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1853/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1854/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1855/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1856/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1857/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1858/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1859/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1860/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1861/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1862/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1863/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1864/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1865/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1866/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1867/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1868/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1869/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1870/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1871/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1872/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1873/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1874/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1875/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1876/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1877/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1878/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1879/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1880/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1881/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1882/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1883/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1884/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1885/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1886/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1887/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1888/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1889/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1890/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1891/2000 236/236 [==============================] - ETA: 0s - loss: 0.4209 - accuracy: 0.81 - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1892/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1893/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1894/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1895/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1896/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1897/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1898/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1899/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1900/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1901/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1902/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1903/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1904/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1905/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1906/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1907/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1908/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1909/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1910/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1911/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1912/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1913/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1914/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1915/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1916/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1917/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1918/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1919/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1920/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1921/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1922/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1923/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1924/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1925/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1926/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1927/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1928/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1929/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1930/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1931/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1932/2000 236/236 [==============================] - 0s 127us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1933/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1934/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1935/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1936/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1937/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1938/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1939/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1940/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1941/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1942/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1943/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1944/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1945/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1946/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1947/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1948/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1949/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1950/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1951/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1952/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1953/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1954/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1955/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1956/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1957/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1958/2000 236/236 [==============================] - 0s 93us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1959/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1960/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1961/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1962/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1963/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1964/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1965/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1966/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1967/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1968/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1969/2000 236/236 [==============================] - 0s 136us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1970/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1971/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1972/2000 236/236 [==============================] - 0s 119us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1973/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1974/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1975/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1976/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1977/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1978/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1979/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1980/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1981/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1982/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1983/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1984/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1985/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1986/2000 236/236 [==============================] - 0s 123us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1987/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1988/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1989/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1990/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1991/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1992/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1993/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1994/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1995/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1996/2000 236/236 [==============================] - 0s 110us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1997/2000 236/236 [==============================] - 0s 106us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1998/2000 236/236 [==============================] - 0s 114us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 1999/2000 236/236 [==============================] - 0s 97us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468 Epoch 2000/2000 236/236 [==============================] - 0s 102us/step - loss: 0.5239 - accuracy: 0.7458 - val_loss: 0.4718 - val_accuracy: 0.7468
In [37]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 2000)
In [38]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
79/79 [==============================] - 0s 63us/step test loss: 0.471761223636096, test accuracy: 0.746835470199585
In [40]:
y_pred = model.predict(X_test)
y_pred_d = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred_d))
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred_d))
Kappa: 0.059523809523809534 AUC ROC: 0.7723132969034608 [[57 4] [16 2]]
KMeans¶
In [102]:
X
Out[102]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.297583 | 1.225637 | -0.367641 | 0.606499 | 0.072373 | -2.029620 | 0.791469 | 0.752018 | 2.268802 | -1.383289 | 0.548279 | 1.903211 | -1.011470 |
| 1 | 0.637676 | -1.507256 | -1.572737 | -0.954161 | -0.857425 | 0.327005 | 0.816764 | 0.214245 | 0.241703 | 0.637066 | 1.601538 | 0.300317 | -0.466779 |
| 2 | 2.236730 | -0.319414 | 0.669910 | -1.918119 | -0.820882 | -2.379333 | -1.570021 | -2.755344 | -2.150610 | -2.528577 | -0.877081 | -0.522248 | -1.429911 |
| 3 | 0.662077 | -0.381499 | 0.111981 | -1.743808 | -1.317593 | -1.348534 | -0.627198 | -1.629882 | -2.075974 | -1.248765 | -1.126014 | -1.316359 | -1.126174 |
| 4 | 0.736502 | 0.112932 | -0.065024 | -1.049458 | -0.408043 | -0.437499 | 0.090831 | -0.852983 | -1.922491 | -0.284365 | 0.210624 | -0.032122 | -0.700183 |
| 5 | 2.044945 | -1.519304 | -0.247242 | -1.575018 | -3.066163 | -3.318978 | -4.228656 | -1.698572 | -1.328177 | -1.585685 | -2.068965 | -1.981423 | -2.570963 |
| 6 | -0.163510 | 0.463882 | 0.433775 | 0.607952 | -1.213428 | 0.398472 | 0.549864 | -0.956001 | -2.046092 | 0.848843 | 0.361883 | -0.369335 | 0.023466 |
| 7 | 0.469947 | -0.206445 | -0.861641 | -1.194743 | -1.342011 | -0.537321 | -0.618801 | 0.066311 | 1.216468 | 2.424672 | 0.407859 | -0.971317 | -0.885313 |
| 8 | 0.884810 | -2.954758 | 0.310482 | -0.346234 | -0.339963 | -0.215837 | -0.238824 | -0.154440 | -0.038298 | 0.157431 | -0.417738 | -0.346934 | -0.136933 |
| 9 | -0.966889 | 1.065377 | -0.166673 | -0.580533 | 0.526040 | 0.508878 | 1.073663 | 0.967893 | 0.083588 | 0.138440 | -0.514643 | 0.318418 | 0.368555 |
| 10 | 0.322674 | -1.252898 | -1.332613 | -0.629526 | -1.062802 | -0.073310 | -0.754358 | -0.444217 | -0.260065 | 0.586226 | -0.084312 | 0.912315 | 0.392903 |
| 11 | -0.198884 | 0.112823 | 0.240285 | 0.472717 | -0.142430 | 0.364603 | 0.036949 | 0.383315 | 0.567300 | -0.213906 | -0.666284 | 0.251886 | 0.001530 |
| 12 | -0.704344 | 0.127023 | 0.716665 | -0.403523 | -0.515524 | -0.315358 | 0.189853 | -0.589153 | -0.264368 | -0.267752 | -0.195456 | 0.069763 | 0.028519 |
| 13 | -0.798804 | -0.117919 | -0.357717 | 0.064237 | -1.152914 | -0.559676 | -0.651647 | -0.039909 | -0.177030 | 0.500161 | -0.273613 | 0.258679 | 0.790680 |
| 14 | -0.078167 | -0.317301 | 0.539934 | 0.021348 | -0.238394 | -1.228167 | -0.600813 | 0.330424 | -0.715325 | 0.747390 | -0.474837 | -0.755240 | -0.089072 |
| 15 | 0.239636 | 0.763042 | 0.457000 | 0.194748 | -0.448390 | -1.142082 | -1.844178 | 0.710890 | -0.411874 | -0.048000 | 0.959817 | 1.034801 | -0.488467 |
| 16 | 0.593005 | -0.028653 | 0.182359 | -0.006019 | 0.143398 | -0.749865 | 0.313923 | 0.942236 | 0.207952 | -0.901225 | -0.147923 | 0.552419 | -1.176684 |
| 17 | -0.565089 | -0.734146 | 0.115715 | 0.669649 | 0.299612 | -0.880228 | 0.070142 | -0.683059 | -0.693274 | 0.872570 | 1.622547 | -0.210766 | 0.369132 |
| 18 | -0.286584 | -1.165487 | -0.511773 | 0.317715 | 0.517783 | -1.379822 | 0.371120 | 0.010478 | -0.697242 | 0.652545 | -0.084622 | -0.479999 | 1.158310 |
| 19 | -0.493038 | -0.532596 | 0.330444 | 0.672695 | 0.252734 | -0.921397 | -1.266250 | 0.422426 | 1.086459 | 2.009229 | 2.043563 | 0.507131 | 0.412127 |
| 20 | 1.367128 | -0.497386 | -0.060729 | 0.760778 | -1.140772 | 0.151619 | -1.896890 | -3.328933 | -1.164662 | -1.274380 | 0.647583 | 1.589549 | 0.963561 |
| 21 | 0.073944 | 0.076483 | 0.128736 | 0.807417 | -1.111030 | -0.134221 | -1.151001 | -1.658868 | -1.130705 | -0.105905 | -0.047689 | -0.710169 | 1.125641 |
| 22 | 0.660587 | -0.157368 | -0.327968 | 0.214143 | -1.218601 | -0.164400 | -1.547244 | -1.835812 | -1.428252 | -0.025563 | 0.297802 | -1.007616 | 0.493018 |
| 23 | 0.629501 | 0.686317 | 0.608046 | -0.676736 | -0.888682 | 0.627145 | -1.099783 | 0.193809 | -0.138455 | -2.486797 | -0.846714 | -2.218162 | -0.746468 |
| 24 | -0.898063 | 2.104109 | 0.850318 | 1.640696 | 0.809305 | 1.055106 | -0.219443 | 0.333777 | -0.393837 | 0.524945 | 0.726660 | -1.038018 | 0.706289 |
| 25 | -1.139909 | 1.564949 | 0.224228 | 1.291732 | 0.521761 | 0.566571 | -0.502128 | 0.174052 | -0.668503 | 0.525822 | 0.608177 | -0.929238 | 0.913787 |
| 26 | -0.670112 | 0.400455 | 0.542697 | 0.702795 | -0.312340 | 0.610331 | 0.039142 | 0.210504 | -0.147396 | 0.558589 | -0.037638 | -1.606275 | -0.202252 |
| 27 | -0.698001 | -0.051148 | 0.596315 | -0.686050 | -0.524681 | 0.775281 | -0.431256 | 0.459523 | 0.649406 | -1.709659 | 0.820167 | -1.716683 | -0.365401 |
| 28 | 0.267840 | 0.993382 | -0.731513 | -0.033601 | -0.525393 | 0.229122 | -0.296022 | 1.310078 | 0.644052 | -1.340101 | 1.103501 | 0.466996 | 0.540105 |
| 29 | -1.497035 | -1.682687 | -2.317594 | -1.006384 | -1.191688 | -1.739630 | -0.583086 | 0.367213 | 0.619588 | -0.474930 | -0.553418 | -0.278936 | -0.651289 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 285 | 0.054160 | 0.170817 | 0.428157 | -0.173562 | 0.450572 | 0.340002 | 0.490456 | 0.213782 | -0.773382 | -0.563043 | 0.363713 | -0.075376 | 0.254277 |
| 286 | -0.837236 | -0.153388 | 0.268665 | 1.306860 | 1.166541 | 1.445964 | 1.199082 | -0.371965 | -0.308775 | -0.231117 | 0.135108 | -0.309420 | -0.311043 |
| 287 | 0.054272 | 0.092607 | -0.112145 | 0.149520 | -0.877907 | 0.167855 | 0.850451 | 0.678919 | -0.080733 | -0.661900 | -0.257044 | -0.389926 | -1.512126 |
| 288 | 1.304506 | -0.939417 | -1.006008 | 1.020838 | -1.215004 | 0.813775 | 1.376113 | -1.476023 | -2.020203 | 0.337259 | 2.404807 | -1.045089 | 0.294137 |
| 289 | -0.159584 | -0.189562 | -0.163770 | 0.245326 | -0.612664 | 0.540468 | 0.129258 | -1.140293 | -1.534282 | -0.446273 | 1.154468 | 0.496372 | -0.844209 |
| 290 | -0.506257 | -0.132656 | -0.153020 | -0.443656 | -1.058433 | -1.070196 | -1.502266 | -0.228195 | 0.011621 | 0.395647 | -0.043527 | -0.074259 | -0.944997 |
| 291 | 0.434058 | 0.398883 | 0.528841 | 1.249494 | -0.183209 | 0.484735 | 0.107864 | -0.036215 | 0.158756 | 0.063774 | -0.015594 | 0.089898 | -0.386434 |
| 292 | 0.539818 | 0.967349 | 0.496940 | 0.788583 | 0.455748 | -0.003488 | 0.155054 | 0.250439 | 0.033537 | -0.103080 | 0.763456 | 0.120334 | -0.561083 |
| 293 | 0.273525 | -0.141184 | 0.986558 | 0.184194 | -1.414155 | -0.887289 | 1.438143 | 0.760252 | -0.135023 | 2.252775 | 1.900066 | -0.624009 | 1.138131 |
| 294 | 1.757881 | -0.657600 | -0.123573 | 1.713741 | 0.702842 | 1.053753 | 0.625178 | -0.475339 | -0.351226 | 0.392355 | 0.055097 | -0.855496 | 0.261994 |
| 295 | 1.094424 | -0.403254 | -0.422590 | 1.461920 | 0.799118 | 0.835687 | 0.944428 | -0.017134 | -0.567998 | 0.548997 | -0.402101 | -1.351402 | 0.844210 |
| 296 | 0.456168 | 0.097137 | 0.082277 | 0.474571 | -0.507174 | -0.722248 | -1.600889 | -0.346317 | 0.125499 | -0.987583 | -0.902738 | -0.334857 | 0.502328 |
| 297 | 0.158964 | 1.272002 | -0.348555 | -0.114827 | -0.295698 | 0.912253 | -0.330632 | 0.873334 | -0.284744 | -1.295494 | -0.870216 | 0.332550 | 1.474750 |
| 298 | -0.259693 | 1.853691 | 0.322474 | 0.273480 | -0.345550 | 0.076148 | -0.807223 | 1.185136 | 0.497571 | 0.246526 | 0.604499 | 0.325991 | 0.648890 |
| 299 | -2.384686 | -0.883216 | 0.899645 | 1.092020 | -0.445358 | 0.965983 | 0.519488 | 0.327030 | 0.842924 | -0.068183 | -0.353463 | 0.674622 | 0.616154 |
| 300 | -1.592394 | -0.161493 | 1.268663 | 1.545345 | -0.785313 | 1.028442 | 0.029964 | 0.687974 | 0.585005 | -0.271009 | -0.309379 | 0.500772 | 0.919246 |
| 301 | -1.609403 | -0.213622 | 1.375684 | 1.572829 | -0.631037 | 1.172929 | 0.158255 | 0.764985 | 0.633586 | -0.171438 | -0.369442 | 0.503642 | 1.007372 |
| 302 | 1.636269 | 0.260324 | 0.678085 | -0.587416 | -0.746366 | -1.079248 | -0.077932 | 0.638303 | 0.636743 | 0.407116 | -0.026192 | -0.277632 | -0.299649 |
| 303 | -0.468490 | 1.262367 | 1.213487 | 0.908453 | 0.995514 | 0.311472 | 0.225458 | -0.034264 | -0.759979 | -0.553620 | -0.608785 | 0.868899 | 0.308641 |
| 304 | 0.142289 | 1.055504 | -0.095146 | 0.415827 | 0.418496 | 0.116729 | 0.275183 | 0.600879 | 0.346584 | 0.250517 | 0.332107 | 0.369426 | -0.246906 |
| 305 | 1.335436 | 0.402811 | -1.381643 | -0.110506 | 0.678178 | 1.421056 | -0.283357 | 0.434156 | 1.225881 | 1.955751 | 0.309648 | -0.785677 | -2.348680 |
| 306 | 3.500613 | -0.323410 | -0.934394 | -3.041308 | 1.822358 | 3.047378 | 1.485650 | 2.669335 | 2.613811 | 1.719411 | -1.458878 | -5.545815 | -3.654033 |
| 307 | 3.518855 | -0.474530 | -0.626225 | -2.369853 | 1.940903 | 2.966850 | 1.650342 | 2.153435 | 2.683362 | 2.298387 | -0.752895 | -5.262247 | -4.609324 |
| 308 | -0.475987 | 0.385400 | 0.283385 | 0.721130 | 0.390121 | 0.767408 | 0.105529 | 1.024463 | 1.051642 | 0.737865 | 1.172001 | 0.939039 | 0.996757 |
| 309 | 1.579671 | 1.097062 | 0.916778 | 0.242411 | 1.350012 | 1.924193 | 1.269541 | 0.800714 | 0.639968 | 1.385723 | -1.326387 | 0.948539 | 2.215496 |
| 310 | -1.373143 | 0.545169 | 2.242534 | 0.151875 | 0.131935 | 1.422049 | -0.049814 | 0.337962 | 1.705104 | 1.020922 | 1.432972 | 1.506929 | 0.326875 |
| 311 | -0.555796 | 0.192435 | -1.486600 | -1.844351 | -0.024486 | 1.541478 | 2.218745 | 1.405936 | 0.718192 | -0.125397 | -0.557874 | -0.139931 | 1.048558 |
| 312 | -0.017409 | 0.098378 | -0.037330 | 0.351157 | -0.857030 | -1.081681 | 0.404292 | 1.175376 | 0.863678 | 1.257200 | 1.194305 | 1.918621 | 0.765312 |
| 313 | -0.318014 | 0.774156 | -0.322540 | -0.221254 | -0.285732 | 0.116133 | 0.130191 | -0.130453 | -0.192462 | 0.885042 | 1.494832 | 0.568910 | -0.113303 |
| 314 | -1.382512 | 0.949410 | -0.199056 | 0.190953 | -1.174835 | -0.995885 | 0.048886 | -0.304819 | -1.529189 | -1.023882 | -1.763085 | -0.975472 | -0.705853 |
315 rows × 13 columns
In [103]:
X.loc[:,'chosen'] = list(y)
X
Out[103]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.297583 | 1.225637 | -0.367641 | 0.606499 | 0.072373 | -2.029620 | 0.791469 | 0.752018 | 2.268802 | -1.383289 | 0.548279 | 1.903211 | -1.011470 | 0 |
| 1 | 0.637676 | -1.507256 | -1.572737 | -0.954161 | -0.857425 | 0.327005 | 0.816764 | 0.214245 | 0.241703 | 0.637066 | 1.601538 | 0.300317 | -0.466779 | 0 |
| 2 | 2.236730 | -0.319414 | 0.669910 | -1.918119 | -0.820882 | -2.379333 | -1.570021 | -2.755344 | -2.150610 | -2.528577 | -0.877081 | -0.522248 | -1.429911 | 0 |
| 3 | 0.662077 | -0.381499 | 0.111981 | -1.743808 | -1.317593 | -1.348534 | -0.627198 | -1.629882 | -2.075974 | -1.248765 | -1.126014 | -1.316359 | -1.126174 | 0 |
| 4 | 0.736502 | 0.112932 | -0.065024 | -1.049458 | -0.408043 | -0.437499 | 0.090831 | -0.852983 | -1.922491 | -0.284365 | 0.210624 | -0.032122 | -0.700183 | 0 |
| 5 | 2.044945 | -1.519304 | -0.247242 | -1.575018 | -3.066163 | -3.318978 | -4.228656 | -1.698572 | -1.328177 | -1.585685 | -2.068965 | -1.981423 | -2.570963 | 0 |
| 6 | -0.163510 | 0.463882 | 0.433775 | 0.607952 | -1.213428 | 0.398472 | 0.549864 | -0.956001 | -2.046092 | 0.848843 | 0.361883 | -0.369335 | 0.023466 | 0 |
| 7 | 0.469947 | -0.206445 | -0.861641 | -1.194743 | -1.342011 | -0.537321 | -0.618801 | 0.066311 | 1.216468 | 2.424672 | 0.407859 | -0.971317 | -0.885313 | 0 |
| 8 | 0.884810 | -2.954758 | 0.310482 | -0.346234 | -0.339963 | -0.215837 | -0.238824 | -0.154440 | -0.038298 | 0.157431 | -0.417738 | -0.346934 | -0.136933 | 0 |
| 9 | -0.966889 | 1.065377 | -0.166673 | -0.580533 | 0.526040 | 0.508878 | 1.073663 | 0.967893 | 0.083588 | 0.138440 | -0.514643 | 0.318418 | 0.368555 | 0 |
| 10 | 0.322674 | -1.252898 | -1.332613 | -0.629526 | -1.062802 | -0.073310 | -0.754358 | -0.444217 | -0.260065 | 0.586226 | -0.084312 | 0.912315 | 0.392903 | 0 |
| 11 | -0.198884 | 0.112823 | 0.240285 | 0.472717 | -0.142430 | 0.364603 | 0.036949 | 0.383315 | 0.567300 | -0.213906 | -0.666284 | 0.251886 | 0.001530 | 0 |
| 12 | -0.704344 | 0.127023 | 0.716665 | -0.403523 | -0.515524 | -0.315358 | 0.189853 | -0.589153 | -0.264368 | -0.267752 | -0.195456 | 0.069763 | 0.028519 | 0 |
| 13 | -0.798804 | -0.117919 | -0.357717 | 0.064237 | -1.152914 | -0.559676 | -0.651647 | -0.039909 | -0.177030 | 0.500161 | -0.273613 | 0.258679 | 0.790680 | 0 |
| 14 | -0.078167 | -0.317301 | 0.539934 | 0.021348 | -0.238394 | -1.228167 | -0.600813 | 0.330424 | -0.715325 | 0.747390 | -0.474837 | -0.755240 | -0.089072 | 0 |
| 15 | 0.239636 | 0.763042 | 0.457000 | 0.194748 | -0.448390 | -1.142082 | -1.844178 | 0.710890 | -0.411874 | -0.048000 | 0.959817 | 1.034801 | -0.488467 | 0 |
| 16 | 0.593005 | -0.028653 | 0.182359 | -0.006019 | 0.143398 | -0.749865 | 0.313923 | 0.942236 | 0.207952 | -0.901225 | -0.147923 | 0.552419 | -1.176684 | 0 |
| 17 | -0.565089 | -0.734146 | 0.115715 | 0.669649 | 0.299612 | -0.880228 | 0.070142 | -0.683059 | -0.693274 | 0.872570 | 1.622547 | -0.210766 | 0.369132 | 0 |
| 18 | -0.286584 | -1.165487 | -0.511773 | 0.317715 | 0.517783 | -1.379822 | 0.371120 | 0.010478 | -0.697242 | 0.652545 | -0.084622 | -0.479999 | 1.158310 | 0 |
| 19 | -0.493038 | -0.532596 | 0.330444 | 0.672695 | 0.252734 | -0.921397 | -1.266250 | 0.422426 | 1.086459 | 2.009229 | 2.043563 | 0.507131 | 0.412127 | 0 |
| 20 | 1.367128 | -0.497386 | -0.060729 | 0.760778 | -1.140772 | 0.151619 | -1.896890 | -3.328933 | -1.164662 | -1.274380 | 0.647583 | 1.589549 | 0.963561 | 0 |
| 21 | 0.073944 | 0.076483 | 0.128736 | 0.807417 | -1.111030 | -0.134221 | -1.151001 | -1.658868 | -1.130705 | -0.105905 | -0.047689 | -0.710169 | 1.125641 | 0 |
| 22 | 0.660587 | -0.157368 | -0.327968 | 0.214143 | -1.218601 | -0.164400 | -1.547244 | -1.835812 | -1.428252 | -0.025563 | 0.297802 | -1.007616 | 0.493018 | 0 |
| 23 | 0.629501 | 0.686317 | 0.608046 | -0.676736 | -0.888682 | 0.627145 | -1.099783 | 0.193809 | -0.138455 | -2.486797 | -0.846714 | -2.218162 | -0.746468 | 0 |
| 24 | -0.898063 | 2.104109 | 0.850318 | 1.640696 | 0.809305 | 1.055106 | -0.219443 | 0.333777 | -0.393837 | 0.524945 | 0.726660 | -1.038018 | 0.706289 | 0 |
| 25 | -1.139909 | 1.564949 | 0.224228 | 1.291732 | 0.521761 | 0.566571 | -0.502128 | 0.174052 | -0.668503 | 0.525822 | 0.608177 | -0.929238 | 0.913787 | 0 |
| 26 | -0.670112 | 0.400455 | 0.542697 | 0.702795 | -0.312340 | 0.610331 | 0.039142 | 0.210504 | -0.147396 | 0.558589 | -0.037638 | -1.606275 | -0.202252 | 0 |
| 27 | -0.698001 | -0.051148 | 0.596315 | -0.686050 | -0.524681 | 0.775281 | -0.431256 | 0.459523 | 0.649406 | -1.709659 | 0.820167 | -1.716683 | -0.365401 | 0 |
| 28 | 0.267840 | 0.993382 | -0.731513 | -0.033601 | -0.525393 | 0.229122 | -0.296022 | 1.310078 | 0.644052 | -1.340101 | 1.103501 | 0.466996 | 0.540105 | 0 |
| 29 | -1.497035 | -1.682687 | -2.317594 | -1.006384 | -1.191688 | -1.739630 | -0.583086 | 0.367213 | 0.619588 | -0.474930 | -0.553418 | -0.278936 | -0.651289 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 285 | 0.054160 | 0.170817 | 0.428157 | -0.173562 | 0.450572 | 0.340002 | 0.490456 | 0.213782 | -0.773382 | -0.563043 | 0.363713 | -0.075376 | 0.254277 | 1 |
| 286 | -0.837236 | -0.153388 | 0.268665 | 1.306860 | 1.166541 | 1.445964 | 1.199082 | -0.371965 | -0.308775 | -0.231117 | 0.135108 | -0.309420 | -0.311043 | 1 |
| 287 | 0.054272 | 0.092607 | -0.112145 | 0.149520 | -0.877907 | 0.167855 | 0.850451 | 0.678919 | -0.080733 | -0.661900 | -0.257044 | -0.389926 | -1.512126 | 1 |
| 288 | 1.304506 | -0.939417 | -1.006008 | 1.020838 | -1.215004 | 0.813775 | 1.376113 | -1.476023 | -2.020203 | 0.337259 | 2.404807 | -1.045089 | 0.294137 | 1 |
| 289 | -0.159584 | -0.189562 | -0.163770 | 0.245326 | -0.612664 | 0.540468 | 0.129258 | -1.140293 | -1.534282 | -0.446273 | 1.154468 | 0.496372 | -0.844209 | 1 |
| 290 | -0.506257 | -0.132656 | -0.153020 | -0.443656 | -1.058433 | -1.070196 | -1.502266 | -0.228195 | 0.011621 | 0.395647 | -0.043527 | -0.074259 | -0.944997 | 1 |
| 291 | 0.434058 | 0.398883 | 0.528841 | 1.249494 | -0.183209 | 0.484735 | 0.107864 | -0.036215 | 0.158756 | 0.063774 | -0.015594 | 0.089898 | -0.386434 | 1 |
| 292 | 0.539818 | 0.967349 | 0.496940 | 0.788583 | 0.455748 | -0.003488 | 0.155054 | 0.250439 | 0.033537 | -0.103080 | 0.763456 | 0.120334 | -0.561083 | 1 |
| 293 | 0.273525 | -0.141184 | 0.986558 | 0.184194 | -1.414155 | -0.887289 | 1.438143 | 0.760252 | -0.135023 | 2.252775 | 1.900066 | -0.624009 | 1.138131 | 1 |
| 294 | 1.757881 | -0.657600 | -0.123573 | 1.713741 | 0.702842 | 1.053753 | 0.625178 | -0.475339 | -0.351226 | 0.392355 | 0.055097 | -0.855496 | 0.261994 | 1 |
| 295 | 1.094424 | -0.403254 | -0.422590 | 1.461920 | 0.799118 | 0.835687 | 0.944428 | -0.017134 | -0.567998 | 0.548997 | -0.402101 | -1.351402 | 0.844210 | 1 |
| 296 | 0.456168 | 0.097137 | 0.082277 | 0.474571 | -0.507174 | -0.722248 | -1.600889 | -0.346317 | 0.125499 | -0.987583 | -0.902738 | -0.334857 | 0.502328 | 1 |
| 297 | 0.158964 | 1.272002 | -0.348555 | -0.114827 | -0.295698 | 0.912253 | -0.330632 | 0.873334 | -0.284744 | -1.295494 | -0.870216 | 0.332550 | 1.474750 | 1 |
| 298 | -0.259693 | 1.853691 | 0.322474 | 0.273480 | -0.345550 | 0.076148 | -0.807223 | 1.185136 | 0.497571 | 0.246526 | 0.604499 | 0.325991 | 0.648890 | 1 |
| 299 | -2.384686 | -0.883216 | 0.899645 | 1.092020 | -0.445358 | 0.965983 | 0.519488 | 0.327030 | 0.842924 | -0.068183 | -0.353463 | 0.674622 | 0.616154 | 1 |
| 300 | -1.592394 | -0.161493 | 1.268663 | 1.545345 | -0.785313 | 1.028442 | 0.029964 | 0.687974 | 0.585005 | -0.271009 | -0.309379 | 0.500772 | 0.919246 | 1 |
| 301 | -1.609403 | -0.213622 | 1.375684 | 1.572829 | -0.631037 | 1.172929 | 0.158255 | 0.764985 | 0.633586 | -0.171438 | -0.369442 | 0.503642 | 1.007372 | 1 |
| 302 | 1.636269 | 0.260324 | 0.678085 | -0.587416 | -0.746366 | -1.079248 | -0.077932 | 0.638303 | 0.636743 | 0.407116 | -0.026192 | -0.277632 | -0.299649 | 1 |
| 303 | -0.468490 | 1.262367 | 1.213487 | 0.908453 | 0.995514 | 0.311472 | 0.225458 | -0.034264 | -0.759979 | -0.553620 | -0.608785 | 0.868899 | 0.308641 | 1 |
| 304 | 0.142289 | 1.055504 | -0.095146 | 0.415827 | 0.418496 | 0.116729 | 0.275183 | 0.600879 | 0.346584 | 0.250517 | 0.332107 | 0.369426 | -0.246906 | 1 |
| 305 | 1.335436 | 0.402811 | -1.381643 | -0.110506 | 0.678178 | 1.421056 | -0.283357 | 0.434156 | 1.225881 | 1.955751 | 0.309648 | -0.785677 | -2.348680 | 1 |
| 306 | 3.500613 | -0.323410 | -0.934394 | -3.041308 | 1.822358 | 3.047378 | 1.485650 | 2.669335 | 2.613811 | 1.719411 | -1.458878 | -5.545815 | -3.654033 | 1 |
| 307 | 3.518855 | -0.474530 | -0.626225 | -2.369853 | 1.940903 | 2.966850 | 1.650342 | 2.153435 | 2.683362 | 2.298387 | -0.752895 | -5.262247 | -4.609324 | 1 |
| 308 | -0.475987 | 0.385400 | 0.283385 | 0.721130 | 0.390121 | 0.767408 | 0.105529 | 1.024463 | 1.051642 | 0.737865 | 1.172001 | 0.939039 | 0.996757 | 1 |
| 309 | 1.579671 | 1.097062 | 0.916778 | 0.242411 | 1.350012 | 1.924193 | 1.269541 | 0.800714 | 0.639968 | 1.385723 | -1.326387 | 0.948539 | 2.215496 | 1 |
| 310 | -1.373143 | 0.545169 | 2.242534 | 0.151875 | 0.131935 | 1.422049 | -0.049814 | 0.337962 | 1.705104 | 1.020922 | 1.432972 | 1.506929 | 0.326875 | 1 |
| 311 | -0.555796 | 0.192435 | -1.486600 | -1.844351 | -0.024486 | 1.541478 | 2.218745 | 1.405936 | 0.718192 | -0.125397 | -0.557874 | -0.139931 | 1.048558 | 1 |
| 312 | -0.017409 | 0.098378 | -0.037330 | 0.351157 | -0.857030 | -1.081681 | 0.404292 | 1.175376 | 0.863678 | 1.257200 | 1.194305 | 1.918621 | 0.765312 | 1 |
| 313 | -0.318014 | 0.774156 | -0.322540 | -0.221254 | -0.285732 | 0.116133 | 0.130191 | -0.130453 | -0.192462 | 0.885042 | 1.494832 | 0.568910 | -0.113303 | 1 |
| 314 | -1.382512 | 0.949410 | -0.199056 | 0.190953 | -1.174835 | -0.995885 | 0.048886 | -0.304819 | -1.529189 | -1.023882 | -1.763085 | -0.975472 | -0.705853 | 1 |
315 rows × 14 columns
In [68]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[68]:
[4095.0, 3677.5272636488644, 3305.67969308587, 3121.4860763676925, 2968.345101601044, 2857.7937836988276, 2716.9450438941003, 2608.9364022273085, 2516.5224429226737, 2419.1503973007284, 2368.727763755893, 2356.6118214758935, 2295.7977011967973, 2209.1952143690924]
In [69]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[69]:
[<matplotlib.lines.Line2D at 0x1b8226f8278>]
In [78]:
avers = []
for k in range(2,15) :
km = KMeans(n_clusters=k, random_state=0, n_init=10)
km.fit(X)
y_clusters = km.labels_
silueta_puntos= silhouette_score(X, y_clusters)
avers.append(silueta_puntos)
avers
Out[78]:
[0.1334599578746985, 0.10421908702341012, 0.09891517752652676, 0.10149681615751224, 0.0927079455677389, 0.07588598939974744, 0.08357922084658045, 0.07482765886529266, 0.08621245822232701, 0.07987632421011658, 0.08304819187882027, 0.0699057551321508, 0.0919264952801006]
In [79]:
plt.figure(figsize=(6,6))
plt.plot(range(2, 15), avers)
Out[79]:
[<matplotlib.lines.Line2D at 0x21d2ab4d898>]
In [114]:
k=2
X_in = X.iloc[:,:-1]
kmeans = KMeans(n_clusters=k, random_state=0, n_init=10)
kmeans.fit(X_in)
y_clusters = kmeans.labels_
cluster_labels = np.unique(y_clusters)
print(Counter(y_clusters))
silueta_puntos= silhouette_samples(X_in, y_clusters, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
yticks = []
colores = ['r', 'g', 'b', 'y', 'o']
for i, c in enumerate(cluster_labels):
silueta_puntos_c = silueta_puntos[y_clusters == c]
silueta_puntos_c.sort()
y_ax_upper += len(silueta_puntos_c)
color = colores[i]
plt.barh(range(y_ax_lower, y_ax_upper), silueta_puntos_c, height=1.0,
edgecolor='none', color=color)
yticks.append((y_ax_lower + y_ax_upper) / 2.)
y_ax_lower += len(silueta_puntos_c)
silueta_promedio = np.mean(silueta_puntos)
plt.axvline(silueta_promedio, color="black", linestyle="--")
plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Coeficiente de silueta')
plt.tight_layout()
# plt.savefig('./figures/silhouette.png', dpi=300)
plt.show()
X_in['Cluster'] = y_clusters
X_in['chosen'] = X['chosen']
stacked = X_in.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(9,9))
Counter({0: 214, 1: 101})
Out[114]:
<matplotlib.axes._subplots.AxesSubplot at 0x21d2a9aeda0>
In [116]:
k=3
X_in = X.iloc[:,:-1]
print(X_in.shape)
kmeans = KMeans(n_clusters=k, random_state=0, n_init=10)
kmeans.fit(X_in)
y_clusters = kmeans.labels_
cluster_labels = np.unique(y_clusters)
print(Counter(y_clusters))
print(df_n_ps[0][y_clusters==2][['artist','song']])
print(np.where(y_clusters==2)[0])
X=X.drop(np.where(y_clusters==2)[0], axis=0)
X_in = X.iloc[:,:-1]
print(X_in.shape)
kmeans = KMeans(n_clusters=k, random_state=0, n_init=10)
kmeans.fit(X_in)
y_clusters = kmeans.labels_
cluster_labels = np.unique(y_clusters)
print(Counter(y_clusters))
silueta_puntos= silhouette_samples(X_in, y_clusters, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
yticks = []
colores = ['r', 'g', 'b', 'y', 'o']
for i, c in enumerate(cluster_labels):
silueta_puntos_c = silueta_puntos[y_clusters == c]
silueta_puntos_c.sort()
y_ax_upper += len(silueta_puntos_c)
color = colores[i]
plt.barh(range(y_ax_lower, y_ax_upper), silueta_puntos_c, height=1.0,
edgecolor='none', color=color)
yticks.append((y_ax_lower + y_ax_upper) / 2.)
y_ax_lower += len(silueta_puntos_c)
silueta_promedio = np.mean(silueta_puntos)
plt.axvline(silueta_promedio, color="black", linestyle="--")
plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Coeficiente de silueta')
plt.tight_layout()
# plt.savefig('./figures/silhouette.png', dpi=300)
plt.show()
X_in['Cluster'] = y_clusters
X_in['chosen'] = X['chosen']
stacked = X_in.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(7,7))
(315, 13)
Counter({1: 158, 0: 151, 2: 6})
artist song
173 Lil Hardin Armstrong Oriental Swing (02-2-38).mp3
174 Lil Hardin Armstrong Oriental Swing (02-2-38).mp3
175 Lil Hardin Armstrong Oriental Swing (02-2-38).mp3
151 Jamiroquai Cloud 9.mp3 ...
121 Satin Jackets Take It From Me.mp3 ...
120 Satin Jackets Take It From Me.mp3 ...
[173 174 175 232 306 307]
(309, 13)
Counter({1: 132, 0: 105, 2: 72})
Out[116]:
<matplotlib.axes._subplots.AxesSubplot at 0x21d2aa67320>
In [115]:
k=4
X_in = X.iloc[:,:-1]
print(X_in.shape)
kmeans = KMeans(n_clusters=k, random_state=0, n_init=10)
kmeans.fit(X_in)
y_clusters = kmeans.labels_
cluster_labels = np.unique(y_clusters)
print(Counter(y_clusters))
silueta_puntos= silhouette_samples(X_in, y_clusters, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
yticks = []
colores = ['r', 'g', 'b', 'y', 'o']
for i, c in enumerate(cluster_labels):
silueta_puntos_c = silueta_puntos[y_clusters == c]
silueta_puntos_c.sort()
y_ax_upper += len(silueta_puntos_c)
color = colores[i]
plt.barh(range(y_ax_lower, y_ax_upper), silueta_puntos_c, height=1.0,
edgecolor='none', color=color)
yticks.append((y_ax_lower + y_ax_upper) / 2.)
y_ax_lower += len(silueta_puntos_c)
silueta_promedio = np.mean(silueta_puntos)
plt.axvline(silueta_promedio, color="black", linestyle="--")
plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Coeficiente de silueta')
plt.tight_layout()
# plt.savefig('./figures/silhouette.png', dpi=300)
plt.show()
X_in['Cluster'] = y_clusters
X_in['chosen'] = X['chosen']
stacked = X_in.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(9,9))
(315, 13)
Counter({0: 138, 2: 121, 1: 50, 3: 6})
In [117]:
k=5
X_in = X.iloc[:,:-1]
kmeans = KMeans(n_clusters=k, random_state=0, n_init=10)
kmeans.fit(X_in)
y_clusters = kmeans.labels_
cluster_labels = np.unique(y_clusters)
print(Counter(y_clusters))
silueta_puntos= silhouette_samples(X_in, y_clusters, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
yticks = []
colores = ['red', 'g', 'b', 'y', 'darkorange']
for i, c in enumerate(cluster_labels):
silueta_puntos_c = silueta_puntos[y_clusters == c]
silueta_puntos_c.sort()
y_ax_upper += len(silueta_puntos_c)
color = colores[i]
plt.barh(range(y_ax_lower, y_ax_upper), silueta_puntos_c, height=1.0,
edgecolor='none', color=color)
yticks.append((y_ax_lower + y_ax_upper) / 2.)
y_ax_lower += len(silueta_puntos_c)
silueta_promedio = np.mean(silueta_puntos)
plt.axvline(silueta_promedio, color="black", linestyle="--")
plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Coeficiente de silueta')
plt.tight_layout()
# plt.savefig('./figures/silhouette.png', dpi=300)
plt.show()
X_in['Cluster'] = y_clusters
X_in['chosen'] = X['chosen']
stacked = X_in.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(9,9))
Counter({3: 96, 4: 78, 1: 61, 2: 44, 0: 30})
Out[117]:
<matplotlib.axes._subplots.AxesSubplot at 0x21d2b1e94e0>
K=3
In [70]:
kmeans_mfcc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[70]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [71]:
kmeans_mfcc.labels_
Out[71]:
array([1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,
1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0,
1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2,
1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 2, 0, 0, 0, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1,
1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 2,
1, 1, 1, 1, 1, 1, 0])
In [72]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[72]:
array([1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,
1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0,
1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2,
1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 2, 0, 0, 0, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1,
1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 2,
1, 1, 1, 1, 1, 1, 0])
In [73]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [74]:
X
Out[74]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.297583 | 1.225637 | -0.367641 | 0.606499 | 0.072373 | -2.029620 | 0.791469 | 0.752018 | 2.268802 | -1.383289 | 0.548279 | 1.903211 | -1.011470 | 1 | 0 |
| 1 | 0.637676 | -1.507256 | -1.572737 | -0.954161 | -0.857425 | 0.327005 | 0.816764 | 0.214245 | 0.241703 | 0.637066 | 1.601538 | 0.300317 | -0.466779 | 0 | 0 |
| 2 | 2.236730 | -0.319414 | 0.669910 | -1.918119 | -0.820882 | -2.379333 | -1.570021 | -2.755344 | -2.150610 | -2.528577 | -0.877081 | -0.522248 | -1.429911 | 0 | 0 |
| 3 | 0.662077 | -0.381499 | 0.111981 | -1.743808 | -1.317593 | -1.348534 | -0.627198 | -1.629882 | -2.075974 | -1.248765 | -1.126014 | -1.316359 | -1.126174 | 0 | 0 |
| 4 | 0.736502 | 0.112932 | -0.065024 | -1.049458 | -0.408043 | -0.437499 | 0.090831 | -0.852983 | -1.922491 | -0.284365 | 0.210624 | -0.032122 | -0.700183 | 0 | 0 |
| 5 | 2.044945 | -1.519304 | -0.247242 | -1.575018 | -3.066163 | -3.318978 | -4.228656 | -1.698572 | -1.328177 | -1.585685 | -2.068965 | -1.981423 | -2.570963 | 0 | 0 |
| 6 | -0.163510 | 0.463882 | 0.433775 | 0.607952 | -1.213428 | 0.398472 | 0.549864 | -0.956001 | -2.046092 | 0.848843 | 0.361883 | -0.369335 | 0.023466 | 0 | 0 |
| 7 | 0.469947 | -0.206445 | -0.861641 | -1.194743 | -1.342011 | -0.537321 | -0.618801 | 0.066311 | 1.216468 | 2.424672 | 0.407859 | -0.971317 | -0.885313 | 0 | 0 |
| 8 | 0.884810 | -2.954758 | 0.310482 | -0.346234 | -0.339963 | -0.215837 | -0.238824 | -0.154440 | -0.038298 | 0.157431 | -0.417738 | -0.346934 | -0.136933 | 0 | 0 |
| 9 | -0.966889 | 1.065377 | -0.166673 | -0.580533 | 0.526040 | 0.508878 | 1.073663 | 0.967893 | 0.083588 | 0.138440 | -0.514643 | 0.318418 | 0.368555 | 1 | 0 |
| 10 | 0.322674 | -1.252898 | -1.332613 | -0.629526 | -1.062802 | -0.073310 | -0.754358 | -0.444217 | -0.260065 | 0.586226 | -0.084312 | 0.912315 | 0.392903 | 0 | 0 |
| 11 | -0.198884 | 0.112823 | 0.240285 | 0.472717 | -0.142430 | 0.364603 | 0.036949 | 0.383315 | 0.567300 | -0.213906 | -0.666284 | 0.251886 | 0.001530 | 1 | 0 |
| 12 | -0.704344 | 0.127023 | 0.716665 | -0.403523 | -0.515524 | -0.315358 | 0.189853 | -0.589153 | -0.264368 | -0.267752 | -0.195456 | 0.069763 | 0.028519 | 0 | 0 |
| 13 | -0.798804 | -0.117919 | -0.357717 | 0.064237 | -1.152914 | -0.559676 | -0.651647 | -0.039909 | -0.177030 | 0.500161 | -0.273613 | 0.258679 | 0.790680 | 0 | 0 |
| 14 | -0.078167 | -0.317301 | 0.539934 | 0.021348 | -0.238394 | -1.228167 | -0.600813 | 0.330424 | -0.715325 | 0.747390 | -0.474837 | -0.755240 | -0.089072 | 0 | 0 |
| 15 | 0.239636 | 0.763042 | 0.457000 | 0.194748 | -0.448390 | -1.142082 | -1.844178 | 0.710890 | -0.411874 | -0.048000 | 0.959817 | 1.034801 | -0.488467 | 0 | 0 |
| 16 | 0.593005 | -0.028653 | 0.182359 | -0.006019 | 0.143398 | -0.749865 | 0.313923 | 0.942236 | 0.207952 | -0.901225 | -0.147923 | 0.552419 | -1.176684 | 0 | 0 |
| 17 | -0.565089 | -0.734146 | 0.115715 | 0.669649 | 0.299612 | -0.880228 | 0.070142 | -0.683059 | -0.693274 | 0.872570 | 1.622547 | -0.210766 | 0.369132 | 0 | 0 |
| 18 | -0.286584 | -1.165487 | -0.511773 | 0.317715 | 0.517783 | -1.379822 | 0.371120 | 0.010478 | -0.697242 | 0.652545 | -0.084622 | -0.479999 | 1.158310 | 0 | 0 |
| 19 | -0.493038 | -0.532596 | 0.330444 | 0.672695 | 0.252734 | -0.921397 | -1.266250 | 0.422426 | 1.086459 | 2.009229 | 2.043563 | 0.507131 | 0.412127 | 1 | 0 |
| 20 | 1.367128 | -0.497386 | -0.060729 | 0.760778 | -1.140772 | 0.151619 | -1.896890 | -3.328933 | -1.164662 | -1.274380 | 0.647583 | 1.589549 | 0.963561 | 0 | 0 |
| 21 | 0.073944 | 0.076483 | 0.128736 | 0.807417 | -1.111030 | -0.134221 | -1.151001 | -1.658868 | -1.130705 | -0.105905 | -0.047689 | -0.710169 | 1.125641 | 0 | 0 |
| 22 | 0.660587 | -0.157368 | -0.327968 | 0.214143 | -1.218601 | -0.164400 | -1.547244 | -1.835812 | -1.428252 | -0.025563 | 0.297802 | -1.007616 | 0.493018 | 0 | 0 |
| 23 | 0.629501 | 0.686317 | 0.608046 | -0.676736 | -0.888682 | 0.627145 | -1.099783 | 0.193809 | -0.138455 | -2.486797 | -0.846714 | -2.218162 | -0.746468 | 0 | 0 |
| 24 | -0.898063 | 2.104109 | 0.850318 | 1.640696 | 0.809305 | 1.055106 | -0.219443 | 0.333777 | -0.393837 | 0.524945 | 0.726660 | -1.038018 | 0.706289 | 1 | 0 |
| 25 | -1.139909 | 1.564949 | 0.224228 | 1.291732 | 0.521761 | 0.566571 | -0.502128 | 0.174052 | -0.668503 | 0.525822 | 0.608177 | -0.929238 | 0.913787 | 1 | 0 |
| 26 | -0.670112 | 0.400455 | 0.542697 | 0.702795 | -0.312340 | 0.610331 | 0.039142 | 0.210504 | -0.147396 | 0.558589 | -0.037638 | -1.606275 | -0.202252 | 1 | 0 |
| 27 | -0.698001 | -0.051148 | 0.596315 | -0.686050 | -0.524681 | 0.775281 | -0.431256 | 0.459523 | 0.649406 | -1.709659 | 0.820167 | -1.716683 | -0.365401 | 0 | 0 |
| 28 | 0.267840 | 0.993382 | -0.731513 | -0.033601 | -0.525393 | 0.229122 | -0.296022 | 1.310078 | 0.644052 | -1.340101 | 1.103501 | 0.466996 | 0.540105 | 1 | 0 |
| 29 | -1.497035 | -1.682687 | -2.317594 | -1.006384 | -1.191688 | -1.739630 | -0.583086 | 0.367213 | 0.619588 | -0.474930 | -0.553418 | -0.278936 | -0.651289 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 285 | 0.054160 | 0.170817 | 0.428157 | -0.173562 | 0.450572 | 0.340002 | 0.490456 | 0.213782 | -0.773382 | -0.563043 | 0.363713 | -0.075376 | 0.254277 | 1 | 1 |
| 286 | -0.837236 | -0.153388 | 0.268665 | 1.306860 | 1.166541 | 1.445964 | 1.199082 | -0.371965 | -0.308775 | -0.231117 | 0.135108 | -0.309420 | -0.311043 | 1 | 1 |
| 287 | 0.054272 | 0.092607 | -0.112145 | 0.149520 | -0.877907 | 0.167855 | 0.850451 | 0.678919 | -0.080733 | -0.661900 | -0.257044 | -0.389926 | -1.512126 | 0 | 1 |
| 288 | 1.304506 | -0.939417 | -1.006008 | 1.020838 | -1.215004 | 0.813775 | 1.376113 | -1.476023 | -2.020203 | 0.337259 | 2.404807 | -1.045089 | 0.294137 | 0 | 1 |
| 289 | -0.159584 | -0.189562 | -0.163770 | 0.245326 | -0.612664 | 0.540468 | 0.129258 | -1.140293 | -1.534282 | -0.446273 | 1.154468 | 0.496372 | -0.844209 | 0 | 1 |
| 290 | -0.506257 | -0.132656 | -0.153020 | -0.443656 | -1.058433 | -1.070196 | -1.502266 | -0.228195 | 0.011621 | 0.395647 | -0.043527 | -0.074259 | -0.944997 | 0 | 1 |
| 291 | 0.434058 | 0.398883 | 0.528841 | 1.249494 | -0.183209 | 0.484735 | 0.107864 | -0.036215 | 0.158756 | 0.063774 | -0.015594 | 0.089898 | -0.386434 | 1 | 1 |
| 292 | 0.539818 | 0.967349 | 0.496940 | 0.788583 | 0.455748 | -0.003488 | 0.155054 | 0.250439 | 0.033537 | -0.103080 | 0.763456 | 0.120334 | -0.561083 | 1 | 1 |
| 293 | 0.273525 | -0.141184 | 0.986558 | 0.184194 | -1.414155 | -0.887289 | 1.438143 | 0.760252 | -0.135023 | 2.252775 | 1.900066 | -0.624009 | 1.138131 | 1 | 1 |
| 294 | 1.757881 | -0.657600 | -0.123573 | 1.713741 | 0.702842 | 1.053753 | 0.625178 | -0.475339 | -0.351226 | 0.392355 | 0.055097 | -0.855496 | 0.261994 | 1 | 1 |
| 295 | 1.094424 | -0.403254 | -0.422590 | 1.461920 | 0.799118 | 0.835687 | 0.944428 | -0.017134 | -0.567998 | 0.548997 | -0.402101 | -1.351402 | 0.844210 | 1 | 1 |
| 296 | 0.456168 | 0.097137 | 0.082277 | 0.474571 | -0.507174 | -0.722248 | -1.600889 | -0.346317 | 0.125499 | -0.987583 | -0.902738 | -0.334857 | 0.502328 | 0 | 1 |
| 297 | 0.158964 | 1.272002 | -0.348555 | -0.114827 | -0.295698 | 0.912253 | -0.330632 | 0.873334 | -0.284744 | -1.295494 | -0.870216 | 0.332550 | 1.474750 | 1 | 1 |
| 298 | -0.259693 | 1.853691 | 0.322474 | 0.273480 | -0.345550 | 0.076148 | -0.807223 | 1.185136 | 0.497571 | 0.246526 | 0.604499 | 0.325991 | 0.648890 | 1 | 1 |
| 299 | -2.384686 | -0.883216 | 0.899645 | 1.092020 | -0.445358 | 0.965983 | 0.519488 | 0.327030 | 0.842924 | -0.068183 | -0.353463 | 0.674622 | 0.616154 | 1 | 1 |
| 300 | -1.592394 | -0.161493 | 1.268663 | 1.545345 | -0.785313 | 1.028442 | 0.029964 | 0.687974 | 0.585005 | -0.271009 | -0.309379 | 0.500772 | 0.919246 | 1 | 1 |
| 301 | -1.609403 | -0.213622 | 1.375684 | 1.572829 | -0.631037 | 1.172929 | 0.158255 | 0.764985 | 0.633586 | -0.171438 | -0.369442 | 0.503642 | 1.007372 | 1 | 1 |
| 302 | 1.636269 | 0.260324 | 0.678085 | -0.587416 | -0.746366 | -1.079248 | -0.077932 | 0.638303 | 0.636743 | 0.407116 | -0.026192 | -0.277632 | -0.299649 | 0 | 1 |
| 303 | -0.468490 | 1.262367 | 1.213487 | 0.908453 | 0.995514 | 0.311472 | 0.225458 | -0.034264 | -0.759979 | -0.553620 | -0.608785 | 0.868899 | 0.308641 | 1 | 1 |
| 304 | 0.142289 | 1.055504 | -0.095146 | 0.415827 | 0.418496 | 0.116729 | 0.275183 | 0.600879 | 0.346584 | 0.250517 | 0.332107 | 0.369426 | -0.246906 | 1 | 1 |
| 305 | 1.335436 | 0.402811 | -1.381643 | -0.110506 | 0.678178 | 1.421056 | -0.283357 | 0.434156 | 1.225881 | 1.955751 | 0.309648 | -0.785677 | -2.348680 | 1 | 1 |
| 306 | 3.500613 | -0.323410 | -0.934394 | -3.041308 | 1.822358 | 3.047378 | 1.485650 | 2.669335 | 2.613811 | 1.719411 | -1.458878 | -5.545815 | -3.654033 | 2 | 1 |
| 307 | 3.518855 | -0.474530 | -0.626225 | -2.369853 | 1.940903 | 2.966850 | 1.650342 | 2.153435 | 2.683362 | 2.298387 | -0.752895 | -5.262247 | -4.609324 | 2 | 1 |
| 308 | -0.475987 | 0.385400 | 0.283385 | 0.721130 | 0.390121 | 0.767408 | 0.105529 | 1.024463 | 1.051642 | 0.737865 | 1.172001 | 0.939039 | 0.996757 | 1 | 1 |
| 309 | 1.579671 | 1.097062 | 0.916778 | 0.242411 | 1.350012 | 1.924193 | 1.269541 | 0.800714 | 0.639968 | 1.385723 | -1.326387 | 0.948539 | 2.215496 | 1 | 1 |
| 310 | -1.373143 | 0.545169 | 2.242534 | 0.151875 | 0.131935 | 1.422049 | -0.049814 | 0.337962 | 1.705104 | 1.020922 | 1.432972 | 1.506929 | 0.326875 | 1 | 1 |
| 311 | -0.555796 | 0.192435 | -1.486600 | -1.844351 | -0.024486 | 1.541478 | 2.218745 | 1.405936 | 0.718192 | -0.125397 | -0.557874 | -0.139931 | 1.048558 | 1 | 1 |
| 312 | -0.017409 | 0.098378 | -0.037330 | 0.351157 | -0.857030 | -1.081681 | 0.404292 | 1.175376 | 0.863678 | 1.257200 | 1.194305 | 1.918621 | 0.765312 | 1 | 1 |
| 313 | -0.318014 | 0.774156 | -0.322540 | -0.221254 | -0.285732 | 0.116133 | 0.130191 | -0.130453 | -0.192462 | 0.885042 | 1.494832 | 0.568910 | -0.113303 | 1 | 1 |
| 314 | -1.382512 | 0.949410 | -0.199056 | 0.190953 | -1.174835 | -0.995885 | 0.048886 | -0.304819 | -1.529189 | -1.023882 | -1.763085 | -0.975472 | -0.705853 | 0 | 1 |
315 rows × 15 columns
In [75]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[75]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b8227479b0>
In [76]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[1]))
Club De Banqueros y Empresarios¶
ANN¶
In [41]:
X = df_n_ps_std_mfcc[1]
In [42]:
y = df_n_ps[1]['chosen']
In [43]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [44]:
X_train.shape
Out[44]:
(191, 13)
In [45]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [46]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [47]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [48]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-48-eff0423a2927> in <module> ----> 1 grid.fit(X_train, y_train) 2 3 print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format( 4 grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100)) 5 end = time.time() # Tiempo después de finalizar el entrenamiento del modelo C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params) 638 error_score=self.error_score) 639 for parameters, (train, test) in product(candidate_params, --> 640 cv.split(X, y, groups))) 641 642 # if one choose to see train score, "out" will contain train score info C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable) 787 # consumption. 788 self._iterating = False --> 789 self.retrieve() 790 # Make sure that we get a last message telling us we are done 791 elapsed_time = time.time() - self._start_time C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in retrieve(self) 697 try: 698 if getattr(self._backend, 'supports_timeout', False): --> 699 self._output.extend(job.get(timeout=self.timeout)) 700 else: 701 self._output.extend(job.get()) C:\ProgramData\Anaconda3\lib\multiprocessing\pool.py in get(self, timeout) 649 650 def get(self, timeout=None): --> 651 self.wait(timeout) 652 if not self.ready(): 653 raise TimeoutError C:\ProgramData\Anaconda3\lib\multiprocessing\pool.py in wait(self, timeout) 646 647 def wait(self, timeout=None): --> 648 self._event.wait(timeout) 649 650 def get(self, timeout=None): C:\ProgramData\Anaconda3\lib\threading.py in wait(self, timeout) 550 signaled = self._flag 551 if not signaled: --> 552 signaled = self._cond.wait(timeout) 553 return signaled 554 C:\ProgramData\Anaconda3\lib\threading.py in wait(self, timeout) 294 try: # restore state no matter what (e.g., KeyboardInterrupt) 295 if timeout is None: --> 296 waiter.acquire() 297 gotit = True 298 else: KeyboardInterrupt:
In [49]:
grid.best_params_={'activation': 'relu', 'hidden_layer_sizes': (20, 20, 20), 'learning_rate_init': 0.009, 'max_iter': 300}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [50]:
input_tensor = Input(shape = (n0,))
In [51]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [52]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [53]:
model.summary()
Model: "model_2" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_3 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_4 (Dense) (None, 20) 280 _________________________________________________________________ dense_5 (Dense) (None, 20) 420 _________________________________________________________________ dense_6 (Dense) (None, 20) 420 _________________________________________________________________ dense_7 (Dense) (None, 1) 21 ================================================================= Total params: 1,141 Trainable params: 1,141 Non-trainable params: 0 _________________________________________________________________
In [54]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), batch_size= 32,
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 191 samples, validate on 64 samples Epoch 1/300 191/191 [==============================] - 0s 2ms/step - loss: 0.6708 - accuracy: 0.6754 - val_loss: 0.5829 - val_accuracy: 0.6875 Epoch 2/300 191/191 [==============================] - 0s 78us/step - loss: 0.5742 - accuracy: 0.7277 - val_loss: 0.5611 - val_accuracy: 0.6875 Epoch 3/300 191/191 [==============================] - 0s 68us/step - loss: 0.5326 - accuracy: 0.7277 - val_loss: 0.5542 - val_accuracy: 0.6875 Epoch 4/300 191/191 [==============================] - 0s 58us/step - loss: 0.4963 - accuracy: 0.7277 - val_loss: 0.5663 - val_accuracy: 0.6875 Epoch 5/300 191/191 [==============================] - 0s 63us/step - loss: 0.4595 - accuracy: 0.7277 - val_loss: 0.5789 - val_accuracy: 0.6875 Epoch 6/300 191/191 [==============================] - 0s 68us/step - loss: 0.4229 - accuracy: 0.7539 - val_loss: 0.5846 - val_accuracy: 0.7188 Epoch 7/300 191/191 [==============================] - 0s 68us/step - loss: 0.3853 - accuracy: 0.8220 - val_loss: 0.5885 - val_accuracy: 0.7500 Epoch 8/300 191/191 [==============================] - 0s 68us/step - loss: 0.3538 - accuracy: 0.8639 - val_loss: 0.6374 - val_accuracy: 0.7656 Epoch 9/300 191/191 [==============================] - 0s 73us/step - loss: 0.3117 - accuracy: 0.8901 - val_loss: 0.5932 - val_accuracy: 0.7812 Epoch 10/300 191/191 [==============================] - 0s 78us/step - loss: 0.2713 - accuracy: 0.9005 - val_loss: 0.6179 - val_accuracy: 0.7812 Epoch 11/300 191/191 [==============================] - 0s 78us/step - loss: 0.2289 - accuracy: 0.9267 - val_loss: 0.6047 - val_accuracy: 0.7969 Epoch 12/300 191/191 [==============================] - 0s 73us/step - loss: 0.1980 - accuracy: 0.9372 - val_loss: 0.5598 - val_accuracy: 0.7812 Epoch 13/300 191/191 [==============================] - 0s 68us/step - loss: 0.1694 - accuracy: 0.9476 - val_loss: 0.6474 - val_accuracy: 0.7969 Epoch 14/300 191/191 [==============================] - 0s 78us/step - loss: 0.1326 - accuracy: 0.9686 - val_loss: 0.5661 - val_accuracy: 0.8125 Epoch 15/300 191/191 [==============================] - 0s 73us/step - loss: 0.1077 - accuracy: 0.9686 - val_loss: 0.8141 - val_accuracy: 0.7969 Epoch 16/300 191/191 [==============================] - 0s 73us/step - loss: 0.0903 - accuracy: 0.9791 - val_loss: 0.6904 - val_accuracy: 0.7969 Epoch 17/300 191/191 [==============================] - 0s 68us/step - loss: 0.0707 - accuracy: 0.9895 - val_loss: 0.8441 - val_accuracy: 0.8125 Epoch 18/300 191/191 [==============================] - 0s 68us/step - loss: 0.0686 - accuracy: 0.9738 - val_loss: 1.0697 - val_accuracy: 0.8281 Epoch 19/300 191/191 [==============================] - 0s 68us/step - loss: 0.0483 - accuracy: 0.9895 - val_loss: 0.9490 - val_accuracy: 0.7969 Epoch 20/300 191/191 [==============================] - 0s 63us/step - loss: 0.0443 - accuracy: 0.9895 - val_loss: 1.0081 - val_accuracy: 0.8281 Epoch 21/300 191/191 [==============================] - 0s 68us/step - loss: 0.0370 - accuracy: 0.9895 - val_loss: 1.0532 - val_accuracy: 0.8281 Epoch 22/300 191/191 [==============================] - 0s 73us/step - loss: 0.0299 - accuracy: 0.9895 - val_loss: 1.1333 - val_accuracy: 0.8125 Epoch 23/300 191/191 [==============================] - 0s 73us/step - loss: 0.0266 - accuracy: 0.9895 - val_loss: 1.2640 - val_accuracy: 0.7969 Epoch 24/300 191/191 [==============================] - 0s 68us/step - loss: 0.0206 - accuracy: 0.9895 - val_loss: 1.3415 - val_accuracy: 0.7812 Epoch 25/300 191/191 [==============================] - 0s 78us/step - loss: 0.0163 - accuracy: 0.9948 - val_loss: 1.3880 - val_accuracy: 0.7812 Epoch 26/300 191/191 [==============================] - 0s 78us/step - loss: 0.0127 - accuracy: 1.0000 - val_loss: 1.4083 - val_accuracy: 0.7812 Epoch 27/300 191/191 [==============================] - 0s 78us/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 1.4624 - val_accuracy: 0.7812 Epoch 28/300 191/191 [==============================] - 0s 78us/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 1.5283 - val_accuracy: 0.7812 Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.0044999998062849045. Epoch 29/300 191/191 [==============================] - 0s 73us/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 1.5350 - val_accuracy: 0.7812 Epoch 30/300 191/191 [==============================] - 0s 68us/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 1.5338 - val_accuracy: 0.7812 Epoch 31/300 191/191 [==============================] - 0s 68us/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 1.5534 - val_accuracy: 0.7812 Epoch 32/300 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.5753 - val_accuracy: 0.7812 Epoch 33/300 191/191 [==============================] - 0s 73us/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 1.5968 - val_accuracy: 0.7812 Epoch 34/300 191/191 [==============================] - 0s 68us/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 1.6156 - val_accuracy: 0.7812 Epoch 35/300 191/191 [==============================] - 0s 52us/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.6331 - val_accuracy: 0.7812 Epoch 36/300 191/191 [==============================] - 0s 63us/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 1.6560 - val_accuracy: 0.7812 Epoch 37/300 191/191 [==============================] - 0s 68us/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.6676 - val_accuracy: 0.7812 Epoch 38/300 191/191 [==============================] - 0s 73us/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 1.6919 - val_accuracy: 0.7812 Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.0022499999031424522. Epoch 39/300 191/191 [==============================] - 0s 68us/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.6996 - val_accuracy: 0.7812 Epoch 40/300 191/191 [==============================] - 0s 73us/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 1.7069 - val_accuracy: 0.7812 Epoch 41/300 191/191 [==============================] - ETA: 0s - loss: 0.0017 - accuracy: 1.00 - 0s 89us/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 1.7129 - val_accuracy: 0.7812 Epoch 42/300 191/191 [==============================] - 0s 105us/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 1.7220 - val_accuracy: 0.7812 Epoch 43/300 191/191 [==============================] - 0s 78us/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.7311 - val_accuracy: 0.7812 Epoch 44/300 191/191 [==============================] - 0s 73us/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.7396 - val_accuracy: 0.7812 Epoch 45/300 191/191 [==============================] - 0s 78us/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 1.7453 - val_accuracy: 0.7812 Epoch 46/300 191/191 [==============================] - 0s 68us/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.7520 - val_accuracy: 0.7812 Epoch 47/300 191/191 [==============================] - 0s 78us/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 1.7591 - val_accuracy: 0.7812 Epoch 48/300 191/191 [==============================] - 0s 73us/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 1.7702 - val_accuracy: 0.7812 Epoch 00048: ReduceLROnPlateau reducing learning rate to 0.0011249999515712261. Epoch 49/300 191/191 [==============================] - 0s 78us/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.7740 - val_accuracy: 0.7812 Epoch 50/300 191/191 [==============================] - 0s 73us/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.7763 - val_accuracy: 0.7812 Epoch 51/300 191/191 [==============================] - 0s 73us/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.7780 - val_accuracy: 0.7812 Epoch 52/300 191/191 [==============================] - 0s 63us/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.7826 - val_accuracy: 0.7812 Epoch 53/300 191/191 [==============================] - 0s 63us/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.7868 - val_accuracy: 0.7812 Epoch 54/300 191/191 [==============================] - 0s 58us/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.7916 - val_accuracy: 0.7812 Epoch 55/300 191/191 [==============================] - 0s 84us/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.7959 - val_accuracy: 0.7812 Epoch 56/300 191/191 [==============================] - 0s 68us/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.7984 - val_accuracy: 0.7812 Epoch 57/300 191/191 [==============================] - 0s 78us/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 1.8032 - val_accuracy: 0.7812 Epoch 58/300 191/191 [==============================] - 0s 68us/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 1.8059 - val_accuracy: 0.7812 Epoch 00058: ReduceLROnPlateau reducing learning rate to 0.0005624999757856131. Epoch 59/300 191/191 [==============================] - 0s 63us/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 1.8081 - val_accuracy: 0.7812 Epoch 60/300 191/191 [==============================] - 0s 63us/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 1.8100 - val_accuracy: 0.7812 Epoch 61/300 191/191 [==============================] - 0s 68us/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 1.8121 - val_accuracy: 0.7812 Epoch 62/300 191/191 [==============================] - 0s 73us/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.8143 - val_accuracy: 0.7812 Epoch 63/300 191/191 [==============================] - 0s 89us/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.8166 - val_accuracy: 0.7812 Epoch 64/300 191/191 [==============================] - 0s 89us/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.8171 - val_accuracy: 0.7812 Epoch 65/300 191/191 [==============================] - 0s 89us/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.8199 - val_accuracy: 0.7812 Epoch 66/300 191/191 [==============================] - 0s 84us/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.8225 - val_accuracy: 0.7812 Epoch 67/300 191/191 [==============================] - 0s 89us/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.8239 - val_accuracy: 0.7812 Epoch 68/300 191/191 [==============================] - 0s 89us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8262 - val_accuracy: 0.7812 Epoch 00068: ReduceLROnPlateau reducing learning rate to 0.00028124998789280653. Epoch 69/300 191/191 [==============================] - ETA: 0s - loss: 0.0024 - accuracy: 1.00 - 0s 99us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8272 - val_accuracy: 0.7812 Epoch 70/300 191/191 [==============================] - 0s 105us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8279 - val_accuracy: 0.7812 Epoch 71/300 191/191 [==============================] - 0s 99us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8292 - val_accuracy: 0.7812 Epoch 72/300 191/191 [==============================] - 0s 94us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8300 - val_accuracy: 0.7812 Epoch 73/300 191/191 [==============================] - 0s 94us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8310 - val_accuracy: 0.7812 Epoch 74/300 191/191 [==============================] - 0s 99us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8319 - val_accuracy: 0.7812 Epoch 75/300 191/191 [==============================] - 0s 89us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8329 - val_accuracy: 0.7812 Epoch 76/300 191/191 [==============================] - 0s 89us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8342 - val_accuracy: 0.7812 Epoch 77/300 191/191 [==============================] - 0s 94us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8347 - val_accuracy: 0.7812 Epoch 78/300 191/191 [==============================] - 0s 99us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8359 - val_accuracy: 0.7812 Epoch 00078: ReduceLROnPlateau reducing learning rate to 0.00014062499394640326. Epoch 79/300 191/191 [==============================] - 0s 94us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8367 - val_accuracy: 0.7812 Epoch 80/300 191/191 [==============================] - 0s 120us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8372 - val_accuracy: 0.7812 Epoch 81/300 191/191 [==============================] - 0s 105us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8375 - val_accuracy: 0.7812 Epoch 82/300 191/191 [==============================] - 0s 105us/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8380 - val_accuracy: 0.7812 Epoch 83/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8386 - val_accuracy: 0.7812 Epoch 84/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8390 - val_accuracy: 0.7812 Epoch 85/300 191/191 [==============================] - 0s 188us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8396 - val_accuracy: 0.7812 Epoch 86/300 191/191 [==============================] - 0s 162us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8402 - val_accuracy: 0.7812 Epoch 87/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8408 - val_accuracy: 0.7812 Epoch 88/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8414 - val_accuracy: 0.7812 Epoch 00088: ReduceLROnPlateau reducing learning rate to 7.031249697320163e-05. Epoch 89/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8415 - val_accuracy: 0.7812 Epoch 90/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8419 - val_accuracy: 0.7812 Epoch 91/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8421 - val_accuracy: 0.7812 Epoch 92/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8425 - val_accuracy: 0.7812 Epoch 93/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8428 - val_accuracy: 0.7812 Epoch 94/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8431 - val_accuracy: 0.7812 Epoch 95/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8433 - val_accuracy: 0.7812 Epoch 96/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8436 - val_accuracy: 0.7812 Epoch 97/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8440 - val_accuracy: 0.7812 Epoch 98/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8443 - val_accuracy: 0.7812 Epoch 00098: ReduceLROnPlateau reducing learning rate to 3.5156248486600816e-05. Epoch 99/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8444 - val_accuracy: 0.7812 Epoch 100/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8445 - val_accuracy: 0.7812 Epoch 101/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8447 - val_accuracy: 0.7812 Epoch 102/300 191/191 [==============================] - 0s 84us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8449 - val_accuracy: 0.7812 Epoch 103/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8450 - val_accuracy: 0.7812 Epoch 104/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8451 - val_accuracy: 0.7812 Epoch 105/300 191/191 [==============================] - 0s 115us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8453 - val_accuracy: 0.7812 Epoch 106/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8455 - val_accuracy: 0.7812 Epoch 107/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8457 - val_accuracy: 0.7812 Epoch 108/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8458 - val_accuracy: 0.7812 Epoch 00108: ReduceLROnPlateau reducing learning rate to 1.7578124243300408e-05. Epoch 109/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8459 - val_accuracy: 0.7812 Epoch 110/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8460 - val_accuracy: 0.7812 Epoch 111/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8461 - val_accuracy: 0.7812 Epoch 112/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8461 - val_accuracy: 0.7812 Epoch 113/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8462 - val_accuracy: 0.7812 Epoch 114/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8463 - val_accuracy: 0.7812 Epoch 115/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8464 - val_accuracy: 0.7812 Epoch 116/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8465 - val_accuracy: 0.7812 Epoch 117/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8466 - val_accuracy: 0.7812 Epoch 118/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8466 - val_accuracy: 0.7812 Epoch 00118: ReduceLROnPlateau reducing learning rate to 8.789062121650204e-06. Epoch 119/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8467 - val_accuracy: 0.7812 Epoch 120/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8467 - val_accuracy: 0.7812 Epoch 121/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8468 - val_accuracy: 0.7812 Epoch 122/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8468 - val_accuracy: 0.7812 Epoch 123/300 191/191 [==============================] - 0s 131us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8469 - val_accuracy: 0.7812 Epoch 124/300 191/191 [==============================] - 0s 120us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8469 - val_accuracy: 0.7812 Epoch 125/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8470 - val_accuracy: 0.7812 Epoch 126/300 191/191 [==============================] - 0s 84us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8470 - val_accuracy: 0.7812 Epoch 127/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8470 - val_accuracy: 0.7812 Epoch 128/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8471 - val_accuracy: 0.7812 Epoch 00128: ReduceLROnPlateau reducing learning rate to 4.394531060825102e-06. Epoch 129/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8471 - val_accuracy: 0.7812 Epoch 130/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8471 - val_accuracy: 0.7812 Epoch 131/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8471 - val_accuracy: 0.7812 Epoch 132/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8472 - val_accuracy: 0.7812 Epoch 133/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8472 - val_accuracy: 0.7812 Epoch 134/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8472 - val_accuracy: 0.7812 Epoch 135/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8472 - val_accuracy: 0.7812 Epoch 136/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8473 - val_accuracy: 0.7812 Epoch 137/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8473 - val_accuracy: 0.7812 Epoch 138/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8473 - val_accuracy: 0.7812 Epoch 00138: ReduceLROnPlateau reducing learning rate to 2.197265530412551e-06. Epoch 139/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8473 - val_accuracy: 0.7812 Epoch 140/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8473 - val_accuracy: 0.7812 Epoch 141/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8473 - val_accuracy: 0.7812 Epoch 142/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8473 - val_accuracy: 0.7812 Epoch 143/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 144/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 145/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 146/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 147/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 148/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 00148: ReduceLROnPlateau reducing learning rate to 1.0986327652062755e-06. Epoch 149/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 150/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 151/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 152/300 191/191 [==============================] - 0s 120us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8474 - val_accuracy: 0.7812 Epoch 153/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 154/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 155/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 156/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 157/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 158/300 191/191 [==============================] - 0s 84us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00158: ReduceLROnPlateau reducing learning rate to 5.493163826031378e-07. Epoch 159/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 160/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 161/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 162/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 163/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 164/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 165/300 191/191 [==============================] - ETA: 0s - loss: 0.0014 - accuracy: 1.00 - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 166/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 167/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 168/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00168: ReduceLROnPlateau reducing learning rate to 2.746581913015689e-07. Epoch 169/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 170/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 171/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 172/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 173/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 174/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 175/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 176/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 177/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 178/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00178: ReduceLROnPlateau reducing learning rate to 1.3732909565078444e-07. Epoch 179/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 180/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 181/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 182/300 191/191 [==============================] - 0s 209us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 183/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 184/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 185/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 186/300 191/191 [==============================] - 0s 126us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 187/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 188/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00188: ReduceLROnPlateau reducing learning rate to 6.866454782539222e-08. Epoch 189/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 190/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 191/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 192/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 193/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 194/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 195/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 196/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 197/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 198/300 191/191 [==============================] - 0s 115us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00198: ReduceLROnPlateau reducing learning rate to 3.433227391269611e-08. Epoch 199/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 200/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 201/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 202/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 203/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 204/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 205/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 206/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 207/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 208/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00208: ReduceLROnPlateau reducing learning rate to 1.7166136956348055e-08. Epoch 209/300 191/191 [==============================] - 0s 115us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 210/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 211/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 212/300 191/191 [==============================] - 0s 84us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 213/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 214/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 215/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 216/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 217/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 218/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00218: ReduceLROnPlateau reducing learning rate to 8.583068478174027e-09. Epoch 219/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 220/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 221/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 222/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 223/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 224/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 225/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 226/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 227/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 228/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00228: ReduceLROnPlateau reducing learning rate to 4.291534239087014e-09. Epoch 229/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 230/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 231/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 232/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 233/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 234/300 191/191 [==============================] - ETA: 0s - loss: 4.6449e-04 - accuracy: 1.00 - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 235/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 236/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 237/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 238/300 191/191 [==============================] - 0s 115us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00238: ReduceLROnPlateau reducing learning rate to 2.145767119543507e-09. Epoch 239/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 240/300 191/191 [==============================] - 0s 188us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 241/300 191/191 [==============================] - 0s 209us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 242/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 243/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 244/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 245/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 246/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 247/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 248/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00248: ReduceLROnPlateau reducing learning rate to 1.0728835597717534e-09. Epoch 249/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 250/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 251/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 252/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 253/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 254/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 255/300 191/191 [==============================] - 0s 120us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 256/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 257/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 258/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00258: ReduceLROnPlateau reducing learning rate to 5.364417798858767e-10. Epoch 259/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 260/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 261/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 262/300 191/191 [==============================] - 0s 115us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 263/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 264/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 265/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 266/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 267/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 268/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00268: ReduceLROnPlateau reducing learning rate to 2.6822088994293836e-10. Epoch 269/300 191/191 [==============================] - 0s 115us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 270/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 271/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 272/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 273/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 274/300 191/191 [==============================] - 0s 89us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 275/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 276/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 277/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 278/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00278: ReduceLROnPlateau reducing learning rate to 1.3411044497146918e-10. Epoch 279/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 280/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 281/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 282/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 283/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 284/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 285/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 286/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 287/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 288/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00288: ReduceLROnPlateau reducing learning rate to 6.705522248573459e-11. Epoch 289/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 290/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 291/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 292/300 191/191 [==============================] - 0s 110us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 293/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 294/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 295/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 296/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 297/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 298/300 191/191 [==============================] - 0s 94us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 00298: ReduceLROnPlateau reducing learning rate to 3.3527611242867295e-11. Epoch 299/300 191/191 [==============================] - 0s 105us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812 Epoch 300/300 191/191 [==============================] - 0s 99us/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8475 - val_accuracy: 0.7812
In [55]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 300)
In [56]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
64/64 [==============================] - 0s 78us/step test loss: 1.8475333452224731, test accuracy: 0.78125
In [57]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.8500000000000001
In [59]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.4285714285714286 [[41 3] [11 9]]
KMeans¶
In [95]:
X
Out[95]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.339415 | 0.847773 | 0.497198 | -0.389310 | 1.225458 | 1.947033 | -0.736267 | 0.492219 | 0.576682 | 1.504697 | -1.796460 | 0.724954 | 0.958600 |
| 1 | 0.587658 | -1.195426 | 0.636375 | 0.199876 | 0.765321 | 0.061181 | 0.379367 | -0.440867 | 0.232893 | 1.339920 | 0.110001 | 0.807525 | 0.815678 |
| 2 | 1.465595 | -2.307943 | 0.354567 | -0.058273 | -1.298853 | -0.811453 | -1.551580 | -3.934320 | -1.079432 | 2.546130 | 1.421407 | 0.639359 | 0.199094 |
| 3 | 0.749403 | -1.690498 | -0.125200 | -1.016135 | 0.825845 | 0.271444 | -0.104786 | -0.992141 | 0.049182 | 1.425948 | -0.343269 | -0.789558 | -0.411898 |
| 4 | -0.280577 | 0.393332 | 0.744917 | 2.411400 | -0.777421 | -0.420018 | 1.258355 | -1.544565 | -0.498071 | 0.421527 | -0.632908 | -0.056846 | -0.072348 |
| 5 | -0.158690 | 0.404891 | -0.147920 | -0.299241 | -0.786974 | 0.697216 | 0.290501 | 0.019739 | -1.468086 | -0.346174 | -0.086965 | 0.026492 | 1.019512 |
| 6 | 1.646777 | 0.772744 | -1.425228 | -0.562610 | -1.556076 | 0.533289 | -0.404271 | 1.676958 | 0.979516 | 0.415548 | 0.544719 | 0.433332 | 0.204271 |
| 7 | 1.124970 | 0.506236 | 0.738993 | 1.984485 | -0.928706 | -0.494097 | -0.707105 | -0.494778 | -1.642929 | 0.207467 | 0.181382 | 2.431721 | 0.848697 |
| 8 | 0.920059 | 1.438862 | -2.048354 | 1.503567 | -2.801303 | 0.567132 | -0.745441 | 0.569519 | 0.130917 | 1.965436 | -0.034797 | 1.164878 | 0.074074 |
| 9 | 0.182544 | 0.310622 | 0.067722 | 0.870138 | 0.168366 | 0.682045 | -0.191296 | -0.144962 | -0.630020 | -0.284032 | -0.315301 | 0.344841 | 0.495167 |
| 10 | 0.168663 | 0.389450 | 0.034360 | 1.213392 | 0.248437 | 0.870618 | -0.460824 | -0.174734 | -0.710502 | -0.228408 | -0.265153 | 0.349416 | 0.584114 |
| 11 | 0.153010 | -0.118336 | 0.639531 | 1.504522 | 0.937909 | 0.356048 | -0.089987 | -0.628522 | 0.064203 | 0.966049 | 0.403915 | -0.943626 | 0.173874 |
| 12 | 0.132578 | 0.261966 | -2.871493 | -3.398160 | -0.256458 | 1.596532 | -0.358711 | 0.175955 | -0.499075 | 0.949085 | 2.235525 | -0.197712 | -0.272366 |
| 13 | 1.094629 | 0.885150 | -1.130672 | -0.083270 | 0.672482 | 0.750453 | -0.863949 | 0.140540 | 0.423312 | -0.305155 | -0.424905 | 0.318660 | 0.885900 |
| 14 | 0.771472 | 0.364448 | -0.454696 | 0.434253 | 0.912699 | 0.745924 | -0.073390 | -0.406473 | 0.450765 | 0.323180 | -0.458826 | -0.132295 | 0.495454 |
| 15 | 0.677561 | 0.166795 | 0.746471 | 0.075191 | 0.867924 | -1.621678 | 0.771146 | -0.067286 | 0.557998 | -0.093593 | 0.020233 | -0.800013 | -0.629188 |
| 16 | -0.032353 | 1.227345 | -0.188580 | 0.927210 | 0.016663 | 1.001867 | -0.473811 | 0.782387 | 1.542760 | -0.345478 | -0.838104 | -0.439443 | 1.179204 |
| 17 | 0.459031 | 1.258961 | -0.329412 | 1.391790 | -0.208888 | 1.059241 | -1.245671 | 0.619153 | 0.245780 | 0.644548 | -0.602629 | -0.928581 | 0.739885 |
| 18 | -0.359172 | 0.051214 | -0.603962 | 0.778896 | 1.630471 | 1.802477 | 1.486205 | -0.140738 | -0.894366 | 0.736624 | 2.114721 | 1.078175 | -0.965785 |
| 19 | 0.209859 | -0.615399 | -0.676895 | 0.735655 | 0.805509 | -0.696793 | 1.073068 | 0.240429 | -0.205934 | -0.759693 | 0.672843 | 0.569482 | -0.455391 |
| 20 | 0.127381 | -0.265099 | -0.258801 | -0.127568 | 0.649447 | 0.244473 | 1.897421 | -0.344616 | -0.593159 | 0.065147 | 1.787607 | 1.219355 | -0.171813 |
| 21 | 1.222717 | 0.409860 | 1.311826 | 0.703873 | 0.322062 | 0.305461 | -0.522644 | -0.750833 | 0.001767 | 0.017953 | 0.254329 | -0.227762 | -0.614790 |
| 22 | 1.173352 | 0.490500 | 0.742825 | -0.028159 | -0.272396 | -0.502733 | -0.759443 | -1.031924 | -0.157975 | 0.075659 | 0.604220 | 0.143298 | -0.001849 |
| 23 | 1.069960 | 0.858822 | -0.795544 | 0.076688 | 0.851875 | 0.735014 | -0.758779 | 0.065595 | 0.532667 | -0.391858 | -0.497019 | 0.240822 | 0.848126 |
| 24 | 0.581377 | -0.804045 | 0.399887 | 1.535671 | 0.245878 | 0.904192 | -0.233991 | -0.925983 | 0.212280 | 0.499535 | -0.024926 | -0.925999 | 1.294925 |
| 25 | 0.161110 | 0.025075 | 0.716318 | 1.532230 | 0.889883 | 0.353167 | -0.058787 | -0.593046 | 0.093773 | 0.927085 | 0.199691 | -0.979872 | 0.232850 |
| 26 | 0.431443 | 0.442713 | 0.259120 | 0.045533 | 0.102675 | 0.367606 | 0.054320 | 0.942924 | 0.180609 | 0.550983 | 0.265291 | 0.321252 | -0.830969 |
| 27 | 0.344525 | -1.140315 | -0.725453 | -0.547965 | 0.449924 | 0.303904 | 1.053624 | 1.051712 | 0.509322 | 0.181611 | -0.519979 | -1.134490 | -1.439105 |
| 28 | -0.041565 | 0.671274 | 0.195143 | 0.247294 | 0.531620 | 1.050124 | 0.311358 | 0.988161 | -0.198869 | 0.387795 | 1.757366 | 1.351684 | 0.194840 |
| 29 | 0.417845 | -1.134173 | -0.760709 | -0.605264 | 0.077464 | 0.533333 | 1.104524 | 2.124971 | 0.083548 | 0.801730 | 0.092534 | -1.281628 | -1.468782 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | 1.532114 | -1.060006 | -0.434145 | -0.999435 | -1.259462 | 0.039140 | -0.802013 | -0.655286 | 0.714448 | 1.005958 | -0.086372 | 0.537392 | 0.054440 |
| 226 | -0.942320 | 1.172080 | 0.506725 | -0.230675 | -0.104635 | 0.898742 | -1.107001 | -1.182148 | -0.940991 | 0.232366 | 1.778224 | 0.975251 | 1.731084 |
| 227 | 1.421974 | 0.631029 | -0.563813 | -0.694595 | -0.673270 | 0.929022 | 0.476907 | -1.025173 | -0.813644 | -0.060006 | -0.738730 | -0.558099 | 0.057654 |
| 228 | -1.473385 | -0.806223 | 1.849423 | -1.252541 | 0.941013 | -0.872947 | -1.812392 | -0.242718 | -0.097212 | -0.510500 | -0.232195 | -0.546399 | 0.945530 |
| 229 | -1.135926 | -0.772372 | 1.164844 | -1.022517 | 0.630202 | -0.496999 | -1.101656 | -0.168921 | -0.295159 | -0.587401 | 0.369033 | -0.266325 | 0.604469 |
| 230 | -1.085049 | 0.879566 | 0.442593 | 0.128917 | 0.393498 | 0.531555 | 0.392194 | 1.418515 | 0.891015 | -0.348926 | -0.756201 | -0.838584 | -0.015971 |
| 231 | -0.352258 | 0.556982 | 0.530520 | 0.443818 | 0.300921 | 0.032128 | -0.797384 | -0.573532 | 0.398084 | 0.328875 | -0.274964 | -1.300920 | 0.254456 |
| 232 | -1.190363 | 0.797356 | 0.758472 | 0.587917 | 0.890540 | 0.471925 | 0.105793 | 0.680721 | 0.230834 | -0.150709 | -0.816744 | -0.470618 | 0.371198 |
| 233 | -0.651003 | -0.586618 | 1.326854 | -0.451354 | 0.507113 | 0.165474 | -0.919675 | -0.448249 | -1.310940 | -1.372737 | 0.406029 | -1.414627 | -0.434858 |
| 234 | -1.459511 | -0.516281 | 1.631699 | -1.141842 | 0.584621 | -0.458541 | -1.428877 | -0.934556 | -0.216455 | -0.049794 | 0.095580 | 0.387068 | 0.693730 |
| 235 | -0.726984 | 0.702447 | 0.798069 | -0.320660 | 0.530902 | 1.019988 | 0.144995 | 0.207847 | 0.039592 | 0.220761 | 0.762941 | 0.575034 | 0.671517 |
| 236 | -0.300986 | -0.404923 | 0.715406 | 0.245380 | -0.427936 | -0.334843 | -0.228084 | -0.330898 | -0.674327 | 0.199560 | 0.827455 | 0.016433 | 0.866789 |
| 237 | -0.736244 | 0.088611 | 0.910051 | 0.437100 | 0.258256 | 0.363828 | -0.415290 | -0.717445 | -0.012727 | 0.436925 | -0.786954 | -1.217376 | 0.352825 |
| 238 | 0.610473 | -2.664315 | 1.303652 | -2.022376 | 1.500032 | -1.280926 | -1.249533 | 0.432111 | -0.768558 | 0.291156 | -0.092312 | 0.053770 | -0.401166 |
| 239 | -2.045424 | -2.954642 | 0.302601 | -0.868092 | -1.038134 | -1.230777 | 0.514329 | 0.057591 | -1.023895 | 0.275395 | -1.450282 | 0.386242 | 0.318763 |
| 240 | 0.329793 | -1.367570 | -1.454329 | -0.207924 | -0.723609 | -0.149025 | -0.085298 | -0.011595 | -0.240239 | -0.009120 | -0.325229 | -0.025722 | 0.114182 |
| 241 | -1.919591 | 1.382172 | -0.134161 | 0.837967 | -0.687780 | 0.944303 | -0.258652 | -0.742178 | 0.386031 | -1.178099 | -1.843543 | -0.710556 | -0.318561 |
| 242 | -2.087669 | 1.400006 | -0.494964 | 0.451717 | -0.759188 | 0.736625 | 0.133121 | -0.196031 | 1.121231 | 0.474128 | -0.345937 | -0.409324 | -0.442069 |
| 243 | -2.131652 | 0.439305 | -0.612226 | 0.854126 | -0.494550 | 0.825299 | 0.301373 | -0.018964 | 0.690556 | -0.078762 | -0.709495 | -0.075857 | -0.418656 |
| 244 | -1.611989 | -0.756403 | -0.410917 | 1.075909 | 0.297336 | -1.317576 | 1.115011 | -0.467065 | -0.768378 | 1.615499 | 1.611125 | -1.018782 | -1.798744 |
| 245 | -0.142010 | 0.000190 | -0.063461 | -0.506353 | -0.386942 | -0.256144 | 0.270621 | -1.497417 | 0.507892 | 0.456828 | -0.431169 | -0.978417 | 0.015849 |
| 246 | -1.263975 | -1.168117 | -1.396090 | -0.312016 | 1.862268 | 1.400290 | 0.646060 | -0.686864 | 0.418524 | -0.069926 | -0.653856 | -0.853617 | -0.106814 |
| 247 | -0.507700 | 0.899825 | 1.510153 | 1.083642 | 2.081451 | 0.589016 | 0.901321 | 0.658808 | 0.152596 | 0.176442 | -0.447633 | 0.287838 | 0.650479 |
| 248 | -0.159768 | 0.518093 | 2.197018 | 0.698491 | 0.476336 | -2.014255 | -1.614667 | -0.397282 | -1.781932 | -0.208894 | 1.650551 | -0.771436 | -0.987237 |
| 249 | -1.037899 | 1.016712 | 2.774230 | 0.665468 | -0.385673 | 0.587263 | -0.121609 | -0.331379 | 0.622484 | -0.387131 | -0.276584 | 0.218207 | 1.689216 |
| 250 | -0.526923 | -1.169944 | 0.474875 | -0.789231 | 0.369827 | -0.537003 | -1.089843 | -0.173366 | -0.023237 | -0.142334 | 0.740065 | 0.813114 | 0.872556 |
| 251 | -0.770856 | -1.024349 | -0.019140 | -0.097521 | 0.092703 | 0.369242 | -0.273901 | 0.190740 | -0.074032 | 0.113055 | 0.140291 | -0.696275 | 0.166679 |
| 252 | -0.905458 | -0.790575 | 0.206164 | -0.723816 | -0.444860 | 0.107833 | -0.734514 | -0.533865 | -0.634334 | 0.320526 | 0.088428 | -0.348210 | 0.347201 |
| 253 | -1.378235 | -0.338405 | 0.016815 | -0.394563 | 0.034043 | 1.023865 | -0.303960 | -1.316121 | 0.198697 | 0.670577 | 0.809574 | 0.580565 | 0.056004 |
| 254 | -0.199959 | -2.035812 | -0.904507 | -1.511975 | -0.437843 | 0.262972 | -1.943788 | -1.963300 | -2.256227 | 0.354369 | -0.039829 | 0.882325 | 0.139307 |
255 rows × 13 columns
In [96]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[96]:
[3315.0, 2972.7888695817974, 2748.18187155972, 2544.9420084212106, 2413.687059384553, 2278.037996783226, 2213.3487507256823, 2123.4282707474663, 2067.8299633414163, 1977.777252698108, 1956.5229777214513, 1880.0296166971755, 1815.5096049846275, 1785.9955747862728]
In [97]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[97]:
[<matplotlib.lines.Line2D at 0x1b8263247f0>]
K=6
In [98]:
kmeans_mfcc = KMeans(n_clusters=6, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[98]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=6, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [99]:
kmeans_mfcc.labels_
Out[99]:
array([4, 2, 2, 2, 0, 1, 1, 0, 1, 4, 4, 4, 1, 4, 4, 3, 4, 4, 4, 4, 4, 0,
0, 4, 4, 4, 4, 3, 4, 3, 4, 4, 3, 1, 3, 1, 4, 4, 3, 1, 1, 1, 4, 4,
2, 1, 1, 1, 4, 1, 1, 1, 4, 3, 3, 4, 4, 1, 4, 1, 0, 4, 4, 4, 3, 3,
3, 4, 3, 0, 4, 2, 1, 4, 0, 4, 4, 3, 3, 0, 0, 2, 1, 4, 1, 0, 0, 3,
4, 1, 4, 4, 4, 4, 1, 1, 0, 3, 3, 3, 4, 1, 1, 4, 1, 4, 0, 3, 1, 1,
1, 0, 3, 2, 4, 4, 0, 1, 5, 3, 4, 0, 0, 2, 1, 0, 0, 0, 2, 2, 2, 2,
4, 4, 0, 0, 2, 2, 2, 4, 4, 4, 2, 2, 2, 3, 0, 4, 1, 3, 3, 3, 3, 3,
4, 3, 1, 0, 0, 0, 1, 1, 0, 2, 2, 0, 3, 0, 1, 0, 0, 0, 0, 1, 3, 2,
4, 4, 1, 2, 4, 3, 2, 3, 4, 4, 4, 1, 2, 3, 3, 0, 2, 2, 2, 1, 0, 1,
4, 2, 0, 4, 3, 3, 2, 3, 5, 5, 2, 3, 3, 2, 0, 3, 0, 2, 4, 2, 1, 2,
1, 3, 4, 4, 4, 1, 0, 1, 2, 2, 3, 0, 4, 2, 2, 4, 0, 2, 2, 2, 1, 3,
4, 4, 2, 2, 2, 4, 0, 0, 2, 2, 2, 2, 2])
In [100]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[100]:
array([4, 2, 2, 2, 0, 1, 1, 0, 1, 4, 4, 4, 1, 4, 4, 3, 4, 4, 4, 4, 4, 0,
0, 4, 4, 4, 4, 3, 4, 3, 4, 4, 3, 1, 3, 1, 4, 4, 3, 1, 1, 1, 4, 4,
2, 1, 1, 1, 4, 1, 1, 1, 4, 3, 3, 4, 4, 1, 4, 1, 0, 4, 4, 4, 3, 3,
3, 4, 3, 0, 4, 2, 1, 4, 0, 4, 4, 3, 3, 0, 0, 2, 1, 4, 1, 0, 0, 3,
4, 1, 4, 4, 4, 4, 1, 1, 0, 3, 3, 3, 4, 1, 1, 4, 1, 4, 0, 3, 1, 1,
1, 0, 3, 2, 4, 4, 0, 1, 5, 3, 4, 0, 0, 2, 1, 0, 0, 0, 2, 2, 2, 2,
4, 4, 0, 0, 2, 2, 2, 4, 4, 4, 2, 2, 2, 3, 0, 4, 1, 3, 3, 3, 3, 3,
4, 3, 1, 0, 0, 0, 1, 1, 0, 2, 2, 0, 3, 0, 1, 0, 0, 0, 0, 1, 3, 2,
4, 4, 1, 2, 4, 3, 2, 3, 4, 4, 4, 1, 2, 3, 3, 0, 2, 2, 2, 1, 0, 1,
4, 2, 0, 4, 3, 3, 2, 3, 5, 5, 2, 3, 3, 2, 0, 3, 0, 2, 4, 2, 1, 2,
1, 3, 4, 4, 4, 1, 0, 1, 2, 2, 3, 0, 4, 2, 2, 4, 0, 2, 2, 2, 1, 3,
4, 4, 2, 2, 2, 4, 0, 0, 2, 2, 2, 2, 2])
In [101]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [102]:
X
Out[102]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.339415 | 0.847773 | 0.497198 | -0.389310 | 1.225458 | 1.947033 | -0.736267 | 0.492219 | 0.576682 | 1.504697 | -1.796460 | 0.724954 | 0.958600 | 4 | 0 |
| 1 | 0.587658 | -1.195426 | 0.636375 | 0.199876 | 0.765321 | 0.061181 | 0.379367 | -0.440867 | 0.232893 | 1.339920 | 0.110001 | 0.807525 | 0.815678 | 2 | 0 |
| 2 | 1.465595 | -2.307943 | 0.354567 | -0.058273 | -1.298853 | -0.811453 | -1.551580 | -3.934320 | -1.079432 | 2.546130 | 1.421407 | 0.639359 | 0.199094 | 2 | 0 |
| 3 | 0.749403 | -1.690498 | -0.125200 | -1.016135 | 0.825845 | 0.271444 | -0.104786 | -0.992141 | 0.049182 | 1.425948 | -0.343269 | -0.789558 | -0.411898 | 2 | 0 |
| 4 | -0.280577 | 0.393332 | 0.744917 | 2.411400 | -0.777421 | -0.420018 | 1.258355 | -1.544565 | -0.498071 | 0.421527 | -0.632908 | -0.056846 | -0.072348 | 0 | 0 |
| 5 | -0.158690 | 0.404891 | -0.147920 | -0.299241 | -0.786974 | 0.697216 | 0.290501 | 0.019739 | -1.468086 | -0.346174 | -0.086965 | 0.026492 | 1.019512 | 1 | 0 |
| 6 | 1.646777 | 0.772744 | -1.425228 | -0.562610 | -1.556076 | 0.533289 | -0.404271 | 1.676958 | 0.979516 | 0.415548 | 0.544719 | 0.433332 | 0.204271 | 1 | 0 |
| 7 | 1.124970 | 0.506236 | 0.738993 | 1.984485 | -0.928706 | -0.494097 | -0.707105 | -0.494778 | -1.642929 | 0.207467 | 0.181382 | 2.431721 | 0.848697 | 0 | 0 |
| 8 | 0.920059 | 1.438862 | -2.048354 | 1.503567 | -2.801303 | 0.567132 | -0.745441 | 0.569519 | 0.130917 | 1.965436 | -0.034797 | 1.164878 | 0.074074 | 1 | 0 |
| 9 | 0.182544 | 0.310622 | 0.067722 | 0.870138 | 0.168366 | 0.682045 | -0.191296 | -0.144962 | -0.630020 | -0.284032 | -0.315301 | 0.344841 | 0.495167 | 4 | 0 |
| 10 | 0.168663 | 0.389450 | 0.034360 | 1.213392 | 0.248437 | 0.870618 | -0.460824 | -0.174734 | -0.710502 | -0.228408 | -0.265153 | 0.349416 | 0.584114 | 4 | 0 |
| 11 | 0.153010 | -0.118336 | 0.639531 | 1.504522 | 0.937909 | 0.356048 | -0.089987 | -0.628522 | 0.064203 | 0.966049 | 0.403915 | -0.943626 | 0.173874 | 4 | 0 |
| 12 | 0.132578 | 0.261966 | -2.871493 | -3.398160 | -0.256458 | 1.596532 | -0.358711 | 0.175955 | -0.499075 | 0.949085 | 2.235525 | -0.197712 | -0.272366 | 1 | 0 |
| 13 | 1.094629 | 0.885150 | -1.130672 | -0.083270 | 0.672482 | 0.750453 | -0.863949 | 0.140540 | 0.423312 | -0.305155 | -0.424905 | 0.318660 | 0.885900 | 4 | 0 |
| 14 | 0.771472 | 0.364448 | -0.454696 | 0.434253 | 0.912699 | 0.745924 | -0.073390 | -0.406473 | 0.450765 | 0.323180 | -0.458826 | -0.132295 | 0.495454 | 4 | 0 |
| 15 | 0.677561 | 0.166795 | 0.746471 | 0.075191 | 0.867924 | -1.621678 | 0.771146 | -0.067286 | 0.557998 | -0.093593 | 0.020233 | -0.800013 | -0.629188 | 3 | 0 |
| 16 | -0.032353 | 1.227345 | -0.188580 | 0.927210 | 0.016663 | 1.001867 | -0.473811 | 0.782387 | 1.542760 | -0.345478 | -0.838104 | -0.439443 | 1.179204 | 4 | 0 |
| 17 | 0.459031 | 1.258961 | -0.329412 | 1.391790 | -0.208888 | 1.059241 | -1.245671 | 0.619153 | 0.245780 | 0.644548 | -0.602629 | -0.928581 | 0.739885 | 4 | 0 |
| 18 | -0.359172 | 0.051214 | -0.603962 | 0.778896 | 1.630471 | 1.802477 | 1.486205 | -0.140738 | -0.894366 | 0.736624 | 2.114721 | 1.078175 | -0.965785 | 4 | 0 |
| 19 | 0.209859 | -0.615399 | -0.676895 | 0.735655 | 0.805509 | -0.696793 | 1.073068 | 0.240429 | -0.205934 | -0.759693 | 0.672843 | 0.569482 | -0.455391 | 4 | 0 |
| 20 | 0.127381 | -0.265099 | -0.258801 | -0.127568 | 0.649447 | 0.244473 | 1.897421 | -0.344616 | -0.593159 | 0.065147 | 1.787607 | 1.219355 | -0.171813 | 4 | 0 |
| 21 | 1.222717 | 0.409860 | 1.311826 | 0.703873 | 0.322062 | 0.305461 | -0.522644 | -0.750833 | 0.001767 | 0.017953 | 0.254329 | -0.227762 | -0.614790 | 0 | 0 |
| 22 | 1.173352 | 0.490500 | 0.742825 | -0.028159 | -0.272396 | -0.502733 | -0.759443 | -1.031924 | -0.157975 | 0.075659 | 0.604220 | 0.143298 | -0.001849 | 0 | 0 |
| 23 | 1.069960 | 0.858822 | -0.795544 | 0.076688 | 0.851875 | 0.735014 | -0.758779 | 0.065595 | 0.532667 | -0.391858 | -0.497019 | 0.240822 | 0.848126 | 4 | 0 |
| 24 | 0.581377 | -0.804045 | 0.399887 | 1.535671 | 0.245878 | 0.904192 | -0.233991 | -0.925983 | 0.212280 | 0.499535 | -0.024926 | -0.925999 | 1.294925 | 4 | 0 |
| 25 | 0.161110 | 0.025075 | 0.716318 | 1.532230 | 0.889883 | 0.353167 | -0.058787 | -0.593046 | 0.093773 | 0.927085 | 0.199691 | -0.979872 | 0.232850 | 4 | 0 |
| 26 | 0.431443 | 0.442713 | 0.259120 | 0.045533 | 0.102675 | 0.367606 | 0.054320 | 0.942924 | 0.180609 | 0.550983 | 0.265291 | 0.321252 | -0.830969 | 4 | 0 |
| 27 | 0.344525 | -1.140315 | -0.725453 | -0.547965 | 0.449924 | 0.303904 | 1.053624 | 1.051712 | 0.509322 | 0.181611 | -0.519979 | -1.134490 | -1.439105 | 3 | 0 |
| 28 | -0.041565 | 0.671274 | 0.195143 | 0.247294 | 0.531620 | 1.050124 | 0.311358 | 0.988161 | -0.198869 | 0.387795 | 1.757366 | 1.351684 | 0.194840 | 4 | 0 |
| 29 | 0.417845 | -1.134173 | -0.760709 | -0.605264 | 0.077464 | 0.533333 | 1.104524 | 2.124971 | 0.083548 | 0.801730 | 0.092534 | -1.281628 | -1.468782 | 3 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | 1.532114 | -1.060006 | -0.434145 | -0.999435 | -1.259462 | 0.039140 | -0.802013 | -0.655286 | 0.714448 | 1.005958 | -0.086372 | 0.537392 | 0.054440 | 1 | 1 |
| 226 | -0.942320 | 1.172080 | 0.506725 | -0.230675 | -0.104635 | 0.898742 | -1.107001 | -1.182148 | -0.940991 | 0.232366 | 1.778224 | 0.975251 | 1.731084 | 0 | 1 |
| 227 | 1.421974 | 0.631029 | -0.563813 | -0.694595 | -0.673270 | 0.929022 | 0.476907 | -1.025173 | -0.813644 | -0.060006 | -0.738730 | -0.558099 | 0.057654 | 1 | 1 |
| 228 | -1.473385 | -0.806223 | 1.849423 | -1.252541 | 0.941013 | -0.872947 | -1.812392 | -0.242718 | -0.097212 | -0.510500 | -0.232195 | -0.546399 | 0.945530 | 2 | 1 |
| 229 | -1.135926 | -0.772372 | 1.164844 | -1.022517 | 0.630202 | -0.496999 | -1.101656 | -0.168921 | -0.295159 | -0.587401 | 0.369033 | -0.266325 | 0.604469 | 2 | 1 |
| 230 | -1.085049 | 0.879566 | 0.442593 | 0.128917 | 0.393498 | 0.531555 | 0.392194 | 1.418515 | 0.891015 | -0.348926 | -0.756201 | -0.838584 | -0.015971 | 3 | 1 |
| 231 | -0.352258 | 0.556982 | 0.530520 | 0.443818 | 0.300921 | 0.032128 | -0.797384 | -0.573532 | 0.398084 | 0.328875 | -0.274964 | -1.300920 | 0.254456 | 0 | 1 |
| 232 | -1.190363 | 0.797356 | 0.758472 | 0.587917 | 0.890540 | 0.471925 | 0.105793 | 0.680721 | 0.230834 | -0.150709 | -0.816744 | -0.470618 | 0.371198 | 4 | 1 |
| 233 | -0.651003 | -0.586618 | 1.326854 | -0.451354 | 0.507113 | 0.165474 | -0.919675 | -0.448249 | -1.310940 | -1.372737 | 0.406029 | -1.414627 | -0.434858 | 2 | 1 |
| 234 | -1.459511 | -0.516281 | 1.631699 | -1.141842 | 0.584621 | -0.458541 | -1.428877 | -0.934556 | -0.216455 | -0.049794 | 0.095580 | 0.387068 | 0.693730 | 2 | 1 |
| 235 | -0.726984 | 0.702447 | 0.798069 | -0.320660 | 0.530902 | 1.019988 | 0.144995 | 0.207847 | 0.039592 | 0.220761 | 0.762941 | 0.575034 | 0.671517 | 4 | 1 |
| 236 | -0.300986 | -0.404923 | 0.715406 | 0.245380 | -0.427936 | -0.334843 | -0.228084 | -0.330898 | -0.674327 | 0.199560 | 0.827455 | 0.016433 | 0.866789 | 0 | 1 |
| 237 | -0.736244 | 0.088611 | 0.910051 | 0.437100 | 0.258256 | 0.363828 | -0.415290 | -0.717445 | -0.012727 | 0.436925 | -0.786954 | -1.217376 | 0.352825 | 2 | 1 |
| 238 | 0.610473 | -2.664315 | 1.303652 | -2.022376 | 1.500032 | -1.280926 | -1.249533 | 0.432111 | -0.768558 | 0.291156 | -0.092312 | 0.053770 | -0.401166 | 2 | 1 |
| 239 | -2.045424 | -2.954642 | 0.302601 | -0.868092 | -1.038134 | -1.230777 | 0.514329 | 0.057591 | -1.023895 | 0.275395 | -1.450282 | 0.386242 | 0.318763 | 2 | 1 |
| 240 | 0.329793 | -1.367570 | -1.454329 | -0.207924 | -0.723609 | -0.149025 | -0.085298 | -0.011595 | -0.240239 | -0.009120 | -0.325229 | -0.025722 | 0.114182 | 1 | 1 |
| 241 | -1.919591 | 1.382172 | -0.134161 | 0.837967 | -0.687780 | 0.944303 | -0.258652 | -0.742178 | 0.386031 | -1.178099 | -1.843543 | -0.710556 | -0.318561 | 3 | 1 |
| 242 | -2.087669 | 1.400006 | -0.494964 | 0.451717 | -0.759188 | 0.736625 | 0.133121 | -0.196031 | 1.121231 | 0.474128 | -0.345937 | -0.409324 | -0.442069 | 4 | 1 |
| 243 | -2.131652 | 0.439305 | -0.612226 | 0.854126 | -0.494550 | 0.825299 | 0.301373 | -0.018964 | 0.690556 | -0.078762 | -0.709495 | -0.075857 | -0.418656 | 4 | 1 |
| 244 | -1.611989 | -0.756403 | -0.410917 | 1.075909 | 0.297336 | -1.317576 | 1.115011 | -0.467065 | -0.768378 | 1.615499 | 1.611125 | -1.018782 | -1.798744 | 2 | 1 |
| 245 | -0.142010 | 0.000190 | -0.063461 | -0.506353 | -0.386942 | -0.256144 | 0.270621 | -1.497417 | 0.507892 | 0.456828 | -0.431169 | -0.978417 | 0.015849 | 2 | 1 |
| 246 | -1.263975 | -1.168117 | -1.396090 | -0.312016 | 1.862268 | 1.400290 | 0.646060 | -0.686864 | 0.418524 | -0.069926 | -0.653856 | -0.853617 | -0.106814 | 2 | 1 |
| 247 | -0.507700 | 0.899825 | 1.510153 | 1.083642 | 2.081451 | 0.589016 | 0.901321 | 0.658808 | 0.152596 | 0.176442 | -0.447633 | 0.287838 | 0.650479 | 4 | 1 |
| 248 | -0.159768 | 0.518093 | 2.197018 | 0.698491 | 0.476336 | -2.014255 | -1.614667 | -0.397282 | -1.781932 | -0.208894 | 1.650551 | -0.771436 | -0.987237 | 0 | 1 |
| 249 | -1.037899 | 1.016712 | 2.774230 | 0.665468 | -0.385673 | 0.587263 | -0.121609 | -0.331379 | 0.622484 | -0.387131 | -0.276584 | 0.218207 | 1.689216 | 0 | 1 |
| 250 | -0.526923 | -1.169944 | 0.474875 | -0.789231 | 0.369827 | -0.537003 | -1.089843 | -0.173366 | -0.023237 | -0.142334 | 0.740065 | 0.813114 | 0.872556 | 2 | 1 |
| 251 | -0.770856 | -1.024349 | -0.019140 | -0.097521 | 0.092703 | 0.369242 | -0.273901 | 0.190740 | -0.074032 | 0.113055 | 0.140291 | -0.696275 | 0.166679 | 2 | 1 |
| 252 | -0.905458 | -0.790575 | 0.206164 | -0.723816 | -0.444860 | 0.107833 | -0.734514 | -0.533865 | -0.634334 | 0.320526 | 0.088428 | -0.348210 | 0.347201 | 2 | 1 |
| 253 | -1.378235 | -0.338405 | 0.016815 | -0.394563 | 0.034043 | 1.023865 | -0.303960 | -1.316121 | 0.198697 | 0.670577 | 0.809574 | 0.580565 | 0.056004 | 2 | 1 |
| 254 | -0.199959 | -2.035812 | -0.904507 | -1.511975 | -0.437843 | 0.262972 | -1.943788 | -1.963300 | -2.256227 | 0.354369 | -0.039829 | 0.882325 | 0.139307 | 2 | 1 |
255 rows × 15 columns
In [103]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[103]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b8263682e8>
In [104]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[2]))
Gramma¶
ANN¶
In [60]:
X = df_n_ps_std_mfcc[2]
In [61]:
y = df_n_ps[2]['chosen']
In [62]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [63]:
X_train.shape
Out[63]:
(162, 13)
In [64]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [65]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [66]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [112]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 30, 30), 'learning_rate_init': 0.007, 'max_iter': 10}, que permiten obtener un Accuracy de 74.07% y un Kappa del 19.05
Tiempo total: 24.44 minutos
In [69]:
grid.best_params_={'activation': 'relu', 'hidden_layer_sizes': (30, 30, 30), 'learning_rate_init': 0.007, 'max_iter': 10}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [70]:
input_tensor = Input(shape = (n0,))
In [71]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [72]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [73]:
model.summary()
Model: "model_3" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_4 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_8 (Dense) (None, 30) 420 _________________________________________________________________ dense_9 (Dense) (None, 30) 930 _________________________________________________________________ dense_10 (Dense) (None, 30) 930 _________________________________________________________________ dense_11 (Dense) (None, 1) 31 ================================================================= Total params: 2,311 Trainable params: 2,311 Non-trainable params: 0 _________________________________________________________________
In [74]:
print(epochs)
10
In [75]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), batch_size= 32,
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 162 samples, validate on 54 samples Epoch 1/10 162/162 [==============================] - 0s 2ms/step - loss: 0.6142 - accuracy: 0.7099 - val_loss: 0.6409 - val_accuracy: 0.6481 Epoch 2/10 162/162 [==============================] - 0s 80us/step - loss: 0.5585 - accuracy: 0.7284 - val_loss: 0.6599 - val_accuracy: 0.6481 Epoch 3/10 162/162 [==============================] - 0s 86us/step - loss: 0.4944 - accuracy: 0.7284 - val_loss: 0.6361 - val_accuracy: 0.6481 Epoch 4/10 162/162 [==============================] - 0s 86us/step - loss: 0.4378 - accuracy: 0.7593 - val_loss: 0.6635 - val_accuracy: 0.6481 Epoch 5/10 162/162 [==============================] - 0s 74us/step - loss: 0.3908 - accuracy: 0.7963 - val_loss: 0.7111 - val_accuracy: 0.7037 Epoch 6/10 162/162 [==============================] - 0s 68us/step - loss: 0.3391 - accuracy: 0.8457 - val_loss: 0.7251 - val_accuracy: 0.6296 Epoch 7/10 162/162 [==============================] - 0s 74us/step - loss: 0.3227 - accuracy: 0.8457 - val_loss: 0.8167 - val_accuracy: 0.6296 Epoch 8/10 162/162 [==============================] - 0s 80us/step - loss: 0.2839 - accuracy: 0.8827 - val_loss: 0.8993 - val_accuracy: 0.7037 Epoch 9/10 162/162 [==============================] - 0s 86us/step - loss: 0.2271 - accuracy: 0.9074 - val_loss: 0.9571 - val_accuracy: 0.6667 Epoch 10/10 162/162 [==============================] - 0s 80us/step - loss: 0.1925 - accuracy: 0.9321 - val_loss: 1.0439 - val_accuracy: 0.6852
In [76]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 10)
In [77]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
54/54 [==============================] - 0s 56us/step test loss: 1.043857611991741, test accuracy: 0.6851851940155029
In [78]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6706766917293233
In [79]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.20450606585788578 [[32 3] [14 5]]
KMeans¶
In [123]:
X
Out[123]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.674917 | 0.169246 | 0.673543 | 1.157142 | -0.633186 | 0.688145 | 0.215883 | -0.452048 | 1.101066 | 0.064017 | -0.153703 | 1.751289 | 0.812723 |
| 1 | 0.277269 | 0.514176 | 0.200398 | 0.988939 | -1.756594 | -0.022788 | -0.235704 | 0.523508 | -0.604231 | 1.188209 | 0.863617 | -0.801768 | 0.229305 |
| 2 | 1.483921 | 0.724793 | 0.473099 | 0.439577 | -0.358096 | -0.452581 | -0.213173 | -0.596057 | -0.767473 | 0.696227 | -0.111259 | -0.370649 | -1.325817 |
| 3 | -0.734008 | -0.683844 | -0.764866 | -0.225060 | -0.261235 | -0.243429 | 0.588768 | 0.874148 | 1.302526 | 0.091256 | -0.600323 | -0.827452 | 0.390838 |
| 4 | -0.834815 | -0.735908 | -1.177596 | -0.093532 | 0.508050 | 0.503458 | 1.380798 | 1.847226 | 1.227896 | 0.017729 | -0.329325 | -0.953249 | -0.125917 |
| 5 | -1.113858 | 2.313247 | 2.338424 | 0.540760 | 1.997911 | -1.396188 | 0.364160 | -0.986730 | 0.116982 | 0.425551 | -0.453775 | 0.268916 | 0.198090 |
| 6 | -0.049155 | 1.019548 | 1.191337 | 0.701078 | 0.841417 | -0.703997 | -0.239014 | 0.884758 | 0.480985 | -0.626873 | 0.508075 | 0.590665 | -0.448967 |
| 7 | -1.022436 | 0.622744 | 0.627286 | 0.206854 | 0.789895 | -0.012269 | -0.954108 | -0.476224 | -0.601871 | -0.794474 | -0.415181 | 0.024060 | 0.299063 |
| 8 | -0.346421 | 0.521689 | -0.035458 | -0.183181 | 1.029652 | -0.422948 | 0.157648 | 0.274630 | 0.353045 | 0.009969 | 1.058129 | 0.259981 | -0.021521 |
| 9 | -0.590254 | 0.458662 | 1.067949 | -0.147777 | 0.785110 | -0.899815 | 0.640103 | 0.578162 | 0.136024 | 0.434690 | 0.528031 | 0.669310 | -0.634947 |
| 10 | 0.067247 | 0.223802 | 1.376567 | -0.118221 | 1.654318 | 0.988767 | 0.958416 | 0.691113 | 0.651209 | -1.129401 | 0.906439 | -0.090883 | -0.385179 |
| 11 | -0.662803 | -0.425347 | -0.906661 | 0.641527 | -1.157491 | 0.162830 | 0.428003 | 0.877985 | 2.072426 | 1.810909 | 0.478599 | -0.107073 | -0.520160 |
| 12 | 0.207925 | 1.931905 | -1.974903 | -2.568754 | -2.569685 | -1.491997 | -0.315430 | -0.538287 | 0.695912 | 0.084372 | 0.028695 | -0.312255 | -0.386997 |
| 13 | -1.789427 | -0.891205 | 0.045638 | 1.194730 | 1.102294 | 1.155199 | 0.047810 | 2.200373 | 3.017003 | 1.942547 | 1.032011 | -0.120930 | -1.909119 |
| 14 | -0.780900 | 0.459108 | -0.495743 | 1.223096 | 0.707236 | -0.551337 | -0.861033 | -0.793874 | 1.998119 | 1.432506 | -0.100890 | -0.116429 | -1.057666 |
| 15 | -1.034563 | 1.536681 | 1.491041 | 0.109475 | 0.271871 | 0.544370 | -0.797182 | 0.119653 | 1.222894 | 1.391755 | 0.392527 | 0.768908 | -0.735165 |
| 16 | -0.381346 | 2.241723 | 0.083946 | -1.446368 | -0.665699 | 0.546860 | 0.837295 | 0.602989 | 0.518379 | 0.326439 | -0.970460 | 0.297350 | 0.320878 |
| 17 | -0.011403 | -0.783092 | 0.665912 | -0.882711 | 0.950063 | -1.443392 | 1.799779 | -1.077081 | 0.714995 | 0.322454 | -1.819793 | 1.682432 | -1.532329 |
| 18 | -1.617916 | 0.548468 | 0.040068 | 0.021068 | 0.781017 | 0.992310 | 0.253640 | -0.344738 | -0.555918 | -0.887811 | 0.176482 | -0.003191 | 0.622949 |
| 19 | -0.537412 | 0.128348 | 0.610943 | -0.633727 | 0.907081 | -0.391947 | 1.899666 | -0.986389 | 1.249064 | 1.384333 | -2.570699 | 2.135887 | -0.427812 |
| 20 | 0.636803 | 0.760531 | -0.118083 | 0.109273 | -0.805302 | 0.286324 | -0.164900 | -0.528374 | 0.268807 | 0.626366 | 0.304320 | 0.726237 | 0.570514 |
| 21 | 0.550926 | 0.936063 | -0.865446 | 0.365123 | 0.084843 | 1.251656 | 1.053639 | 0.703424 | 0.100402 | 0.154559 | 0.495301 | -0.203894 | -0.338852 |
| 22 | 0.000462 | 0.578434 | -0.015217 | 0.442323 | -0.153009 | 0.090108 | 0.305506 | 0.584208 | 0.174651 | -0.264088 | 0.347376 | 0.636010 | 0.517943 |
| 23 | 0.083874 | -0.293534 | -0.485861 | 0.412663 | -0.162491 | -0.059849 | -0.749250 | -0.788638 | -1.514498 | -0.236889 | 0.140002 | -0.164718 | 0.611117 |
| 24 | 0.908136 | 0.233711 | -1.075967 | 0.843554 | 1.180479 | 0.233656 | -0.393576 | 0.409481 | -0.653717 | -1.289551 | 0.026014 | 0.824044 | 0.434511 |
| 25 | 0.265049 | 0.397559 | 0.003810 | 1.195218 | -0.144563 | -1.172408 | 0.045381 | 1.297725 | -0.617515 | -0.190867 | 0.048723 | 0.277567 | -0.245827 |
| 26 | 0.419617 | 0.219257 | -1.249181 | -0.589066 | 0.167388 | 0.746567 | -0.263331 | -1.081856 | -1.507797 | -2.415256 | -0.536913 | 1.033723 | 1.595140 |
| 27 | -0.407925 | -0.502431 | -2.226256 | -1.581302 | -0.733748 | -0.615724 | 0.412099 | 1.100990 | 1.504300 | -1.163924 | -0.737084 | 0.082677 | 0.211950 |
| 28 | -0.250483 | -2.083167 | -1.449789 | -0.233210 | -1.132840 | -1.114482 | -0.838611 | 1.091598 | -0.248949 | 1.322608 | 0.272731 | -0.720656 | -0.338540 |
| 29 | -1.146888 | 1.617561 | 0.885685 | 2.161755 | 0.393283 | -0.057043 | -2.032547 | -2.796111 | 1.361183 | 1.370199 | -1.557977 | 0.512374 | 1.394544 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | -1.007290 | -0.872850 | -0.374124 | -0.254709 | -1.117777 | -1.026836 | 0.501806 | 1.604268 | 1.418314 | -1.021132 | -0.287873 | -1.781727 | -0.747651 |
| 187 | -0.285121 | 0.118211 | 0.478011 | 0.861493 | -1.780510 | -0.208885 | -1.164306 | 0.685729 | 0.357957 | -0.226263 | 1.513265 | -0.684050 | 0.458783 |
| 188 | 0.467571 | -1.645745 | -0.541507 | -1.435183 | -0.639321 | -0.479558 | 1.424031 | 0.792277 | 0.489618 | -1.363609 | -1.985852 | -1.020878 | -0.797356 |
| 189 | -0.071747 | -1.954799 | -0.239393 | -1.169079 | -0.773953 | 0.589966 | 1.659844 | 0.265664 | -0.650200 | -1.717119 | -1.918698 | -1.463219 | -0.447396 |
| 190 | 0.406083 | -2.324572 | 1.268557 | -1.773164 | 0.220142 | -1.551887 | 1.338403 | -0.217135 | 0.387651 | 0.321311 | -1.062866 | -1.614390 | -3.086337 |
| 191 | -0.781221 | 1.062669 | 1.057162 | 0.149736 | -0.636136 | 0.428468 | -0.863911 | -0.159688 | -0.045775 | 0.330912 | 0.561954 | 0.363860 | -1.261525 |
| 192 | 0.471597 | 2.019680 | 2.368481 | -1.171548 | -2.104846 | -0.670947 | -1.217913 | 0.354069 | 0.841890 | -0.529207 | 0.300156 | 1.365068 | 1.105093 |
| 193 | -0.786022 | -0.229684 | 0.421340 | -0.309174 | -0.311318 | 1.204237 | 0.287163 | -0.259205 | -0.106656 | 0.412345 | 0.480928 | -0.201399 | -0.613341 |
| 194 | 0.071267 | -0.794887 | 0.430112 | 0.394775 | -0.746468 | 0.547360 | -0.308259 | -0.083362 | -0.498384 | 0.517608 | -0.616623 | -0.235300 | 0.616686 |
| 195 | -0.430296 | -0.963686 | -0.372758 | 0.074908 | -1.551444 | -2.289278 | -0.996792 | -0.144677 | 0.236885 | 0.775027 | 0.825371 | 0.497421 | -0.293443 |
| 196 | -0.295903 | -1.246841 | -0.389881 | 0.186413 | -0.827904 | -1.740339 | -1.375256 | -0.532361 | -0.762533 | -0.840235 | 0.492296 | 0.528822 | -0.126196 |
| 197 | 0.479442 | -2.526563 | -2.233640 | -1.311649 | -0.114249 | 0.148620 | 1.711025 | 0.415772 | -2.224830 | -1.067674 | 1.273042 | 0.432741 | -1.020345 |
| 198 | -0.472461 | -0.316221 | -2.118686 | -0.390396 | -0.247602 | -0.668064 | -0.201965 | -0.506232 | -0.902628 | -1.005551 | 0.272601 | 0.248004 | -0.241612 |
| 199 | -0.006912 | -1.450305 | -0.125795 | -1.995008 | -1.440314 | -0.787148 | 1.230185 | -1.801522 | -0.524097 | -0.296890 | 0.146555 | -0.059935 | -1.742924 |
| 200 | -1.045376 | 0.843938 | -1.177992 | -1.026041 | -0.442183 | 0.610204 | 1.906959 | 0.601365 | 1.149266 | 1.040025 | 1.510768 | 1.525596 | 0.008502 |
| 201 | -1.160197 | 0.899099 | -1.084036 | -0.745060 | -0.704154 | 0.815025 | 1.572385 | 0.694718 | 1.195502 | 1.357973 | 1.719920 | 1.339687 | 0.346437 |
| 202 | 0.846379 | -0.292164 | -2.285100 | -0.590310 | -1.342705 | -0.365748 | -0.736446 | 1.066510 | 1.737515 | 0.368654 | 0.546029 | 0.404179 | -1.048654 |
| 203 | 0.365059 | -0.089335 | -0.757014 | -0.150651 | 0.520638 | 0.383469 | 1.170308 | 0.915512 | -1.099501 | -0.480174 | -0.052102 | 0.105614 | -0.010947 |
| 204 | 1.230293 | 1.978904 | 0.273786 | -0.096460 | 0.345688 | 0.887889 | -0.409813 | -0.373808 | 0.228442 | 0.696266 | 0.267416 | 0.520666 | 1.007514 |
| 205 | -0.042146 | 0.277253 | 0.012741 | -0.226784 | -0.676665 | -0.286922 | 0.799616 | -0.357473 | -0.730921 | 0.222981 | 0.053531 | 0.062325 | 0.479153 |
| 206 | -0.520687 | -0.536310 | 0.241607 | -0.135026 | 0.085343 | 1.155527 | 1.524244 | -0.261140 | -0.801648 | 0.645204 | 0.296049 | 1.047521 | 0.226221 |
| 207 | -0.563298 | -0.749857 | 0.723480 | 0.034849 | 0.249757 | 0.248513 | 0.065331 | 0.789506 | 0.591196 | 0.578037 | -0.360289 | 1.288016 | 0.495527 |
| 208 | 0.959455 | -2.318903 | -0.452832 | -0.892660 | -1.054730 | 1.504668 | -1.691862 | -1.997066 | -1.458509 | 0.390715 | 0.578679 | 0.194999 | -0.104548 |
| 209 | -0.388117 | 0.504159 | 0.113071 | 0.183221 | -0.043271 | 0.766480 | -0.092855 | -1.130498 | 0.529058 | -0.199386 | 0.035560 | -0.333834 | 0.078931 |
| 210 | 0.756581 | 0.841258 | 1.471905 | -0.630055 | -1.095463 | 3.845135 | -0.131687 | -1.562759 | -0.363967 | -0.026532 | 1.803300 | -4.065727 | -1.024033 |
| 211 | 0.113169 | -0.411080 | 0.219643 | 0.091081 | -0.447327 | -0.962500 | -0.624101 | -1.077007 | 0.481174 | -0.271821 | -0.038339 | -0.466845 | 0.707298 |
| 212 | 0.854842 | 0.556052 | 1.002658 | 0.968377 | 0.557854 | -0.579130 | -0.854195 | 1.514705 | 1.589294 | 0.351534 | -0.962939 | 0.326626 | 0.297482 |
| 213 | 1.087836 | -1.091223 | 1.963519 | -0.088209 | 0.598615 | 0.937138 | -1.807416 | 2.031932 | 0.230433 | 0.443770 | -0.751601 | 0.601455 | 0.233461 |
| 214 | -0.595874 | 0.009907 | -0.748990 | -0.070101 | 1.262995 | 1.821824 | 0.850657 | 0.030762 | 0.529494 | -0.624150 | -1.080924 | -0.189073 | 0.819318 |
| 215 | -0.002561 | 0.856829 | -0.237776 | -1.449113 | -0.069162 | 0.539513 | 2.378866 | 0.711870 | -1.305789 | -0.778617 | -0.872224 | -2.333142 | -1.454909 |
216 rows × 13 columns
In [124]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[124]:
[2808.0, 2521.584040394968, 2363.9652951918915, 2235.4561240552457, 2121.3700883394513, 2055.378171433382, 1978.4853026183964, 1920.0476199123686, 1870.5957280462892, 1801.6400494660393, 1755.8728853091284, 1733.9521869345613, 1708.3667929415192, 1622.6522007070782]
In [125]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[125]:
[<matplotlib.lines.Line2D at 0x1b827a6ff60>]
K=2
In [126]:
kmeans_mfcc = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[126]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [127]:
kmeans_mfcc.labels_
Out[127]:
array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1,
0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0])
In [128]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[128]:
array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1,
0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0])
In [129]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [130]:
X
Out[130]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.674917 | 0.169246 | 0.673543 | 1.157142 | -0.633186 | 0.688145 | 0.215883 | -0.452048 | 1.101066 | 0.064017 | -0.153703 | 1.751289 | 0.812723 | 1 | 0 |
| 1 | 0.277269 | 0.514176 | 0.200398 | 0.988939 | -1.756594 | -0.022788 | -0.235704 | 0.523508 | -0.604231 | 1.188209 | 0.863617 | -0.801768 | 0.229305 | 1 | 0 |
| 2 | 1.483921 | 0.724793 | 0.473099 | 0.439577 | -0.358096 | -0.452581 | -0.213173 | -0.596057 | -0.767473 | 0.696227 | -0.111259 | -0.370649 | -1.325817 | 0 | 0 |
| 3 | -0.734008 | -0.683844 | -0.764866 | -0.225060 | -0.261235 | -0.243429 | 0.588768 | 0.874148 | 1.302526 | 0.091256 | -0.600323 | -0.827452 | 0.390838 | 0 | 0 |
| 4 | -0.834815 | -0.735908 | -1.177596 | -0.093532 | 0.508050 | 0.503458 | 1.380798 | 1.847226 | 1.227896 | 0.017729 | -0.329325 | -0.953249 | -0.125917 | 0 | 0 |
| 5 | -1.113858 | 2.313247 | 2.338424 | 0.540760 | 1.997911 | -1.396188 | 0.364160 | -0.986730 | 0.116982 | 0.425551 | -0.453775 | 0.268916 | 0.198090 | 1 | 0 |
| 6 | -0.049155 | 1.019548 | 1.191337 | 0.701078 | 0.841417 | -0.703997 | -0.239014 | 0.884758 | 0.480985 | -0.626873 | 0.508075 | 0.590665 | -0.448967 | 1 | 0 |
| 7 | -1.022436 | 0.622744 | 0.627286 | 0.206854 | 0.789895 | -0.012269 | -0.954108 | -0.476224 | -0.601871 | -0.794474 | -0.415181 | 0.024060 | 0.299063 | 1 | 0 |
| 8 | -0.346421 | 0.521689 | -0.035458 | -0.183181 | 1.029652 | -0.422948 | 0.157648 | 0.274630 | 0.353045 | 0.009969 | 1.058129 | 0.259981 | -0.021521 | 1 | 0 |
| 9 | -0.590254 | 0.458662 | 1.067949 | -0.147777 | 0.785110 | -0.899815 | 0.640103 | 0.578162 | 0.136024 | 0.434690 | 0.528031 | 0.669310 | -0.634947 | 1 | 0 |
| 10 | 0.067247 | 0.223802 | 1.376567 | -0.118221 | 1.654318 | 0.988767 | 0.958416 | 0.691113 | 0.651209 | -1.129401 | 0.906439 | -0.090883 | -0.385179 | 1 | 0 |
| 11 | -0.662803 | -0.425347 | -0.906661 | 0.641527 | -1.157491 | 0.162830 | 0.428003 | 0.877985 | 2.072426 | 1.810909 | 0.478599 | -0.107073 | -0.520160 | 1 | 0 |
| 12 | 0.207925 | 1.931905 | -1.974903 | -2.568754 | -2.569685 | -1.491997 | -0.315430 | -0.538287 | 0.695912 | 0.084372 | 0.028695 | -0.312255 | -0.386997 | 0 | 0 |
| 13 | -1.789427 | -0.891205 | 0.045638 | 1.194730 | 1.102294 | 1.155199 | 0.047810 | 2.200373 | 3.017003 | 1.942547 | 1.032011 | -0.120930 | -1.909119 | 1 | 0 |
| 14 | -0.780900 | 0.459108 | -0.495743 | 1.223096 | 0.707236 | -0.551337 | -0.861033 | -0.793874 | 1.998119 | 1.432506 | -0.100890 | -0.116429 | -1.057666 | 1 | 0 |
| 15 | -1.034563 | 1.536681 | 1.491041 | 0.109475 | 0.271871 | 0.544370 | -0.797182 | 0.119653 | 1.222894 | 1.391755 | 0.392527 | 0.768908 | -0.735165 | 1 | 0 |
| 16 | -0.381346 | 2.241723 | 0.083946 | -1.446368 | -0.665699 | 0.546860 | 0.837295 | 0.602989 | 0.518379 | 0.326439 | -0.970460 | 0.297350 | 0.320878 | 1 | 0 |
| 17 | -0.011403 | -0.783092 | 0.665912 | -0.882711 | 0.950063 | -1.443392 | 1.799779 | -1.077081 | 0.714995 | 0.322454 | -1.819793 | 1.682432 | -1.532329 | 0 | 0 |
| 18 | -1.617916 | 0.548468 | 0.040068 | 0.021068 | 0.781017 | 0.992310 | 0.253640 | -0.344738 | -0.555918 | -0.887811 | 0.176482 | -0.003191 | 0.622949 | 1 | 0 |
| 19 | -0.537412 | 0.128348 | 0.610943 | -0.633727 | 0.907081 | -0.391947 | 1.899666 | -0.986389 | 1.249064 | 1.384333 | -2.570699 | 2.135887 | -0.427812 | 1 | 0 |
| 20 | 0.636803 | 0.760531 | -0.118083 | 0.109273 | -0.805302 | 0.286324 | -0.164900 | -0.528374 | 0.268807 | 0.626366 | 0.304320 | 0.726237 | 0.570514 | 1 | 0 |
| 21 | 0.550926 | 0.936063 | -0.865446 | 0.365123 | 0.084843 | 1.251656 | 1.053639 | 0.703424 | 0.100402 | 0.154559 | 0.495301 | -0.203894 | -0.338852 | 1 | 0 |
| 22 | 0.000462 | 0.578434 | -0.015217 | 0.442323 | -0.153009 | 0.090108 | 0.305506 | 0.584208 | 0.174651 | -0.264088 | 0.347376 | 0.636010 | 0.517943 | 1 | 0 |
| 23 | 0.083874 | -0.293534 | -0.485861 | 0.412663 | -0.162491 | -0.059849 | -0.749250 | -0.788638 | -1.514498 | -0.236889 | 0.140002 | -0.164718 | 0.611117 | 1 | 0 |
| 24 | 0.908136 | 0.233711 | -1.075967 | 0.843554 | 1.180479 | 0.233656 | -0.393576 | 0.409481 | -0.653717 | -1.289551 | 0.026014 | 0.824044 | 0.434511 | 1 | 0 |
| 25 | 0.265049 | 0.397559 | 0.003810 | 1.195218 | -0.144563 | -1.172408 | 0.045381 | 1.297725 | -0.617515 | -0.190867 | 0.048723 | 0.277567 | -0.245827 | 1 | 0 |
| 26 | 0.419617 | 0.219257 | -1.249181 | -0.589066 | 0.167388 | 0.746567 | -0.263331 | -1.081856 | -1.507797 | -2.415256 | -0.536913 | 1.033723 | 1.595140 | 1 | 0 |
| 27 | -0.407925 | -0.502431 | -2.226256 | -1.581302 | -0.733748 | -0.615724 | 0.412099 | 1.100990 | 1.504300 | -1.163924 | -0.737084 | 0.082677 | 0.211950 | 0 | 0 |
| 28 | -0.250483 | -2.083167 | -1.449789 | -0.233210 | -1.132840 | -1.114482 | -0.838611 | 1.091598 | -0.248949 | 1.322608 | 0.272731 | -0.720656 | -0.338540 | 0 | 0 |
| 29 | -1.146888 | 1.617561 | 0.885685 | 2.161755 | 0.393283 | -0.057043 | -2.032547 | -2.796111 | 1.361183 | 1.370199 | -1.557977 | 0.512374 | 1.394544 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | -1.007290 | -0.872850 | -0.374124 | -0.254709 | -1.117777 | -1.026836 | 0.501806 | 1.604268 | 1.418314 | -1.021132 | -0.287873 | -1.781727 | -0.747651 | 0 | 1 |
| 187 | -0.285121 | 0.118211 | 0.478011 | 0.861493 | -1.780510 | -0.208885 | -1.164306 | 0.685729 | 0.357957 | -0.226263 | 1.513265 | -0.684050 | 0.458783 | 1 | 1 |
| 188 | 0.467571 | -1.645745 | -0.541507 | -1.435183 | -0.639321 | -0.479558 | 1.424031 | 0.792277 | 0.489618 | -1.363609 | -1.985852 | -1.020878 | -0.797356 | 0 | 1 |
| 189 | -0.071747 | -1.954799 | -0.239393 | -1.169079 | -0.773953 | 0.589966 | 1.659844 | 0.265664 | -0.650200 | -1.717119 | -1.918698 | -1.463219 | -0.447396 | 0 | 1 |
| 190 | 0.406083 | -2.324572 | 1.268557 | -1.773164 | 0.220142 | -1.551887 | 1.338403 | -0.217135 | 0.387651 | 0.321311 | -1.062866 | -1.614390 | -3.086337 | 0 | 1 |
| 191 | -0.781221 | 1.062669 | 1.057162 | 0.149736 | -0.636136 | 0.428468 | -0.863911 | -0.159688 | -0.045775 | 0.330912 | 0.561954 | 0.363860 | -1.261525 | 1 | 1 |
| 192 | 0.471597 | 2.019680 | 2.368481 | -1.171548 | -2.104846 | -0.670947 | -1.217913 | 0.354069 | 0.841890 | -0.529207 | 0.300156 | 1.365068 | 1.105093 | 1 | 1 |
| 193 | -0.786022 | -0.229684 | 0.421340 | -0.309174 | -0.311318 | 1.204237 | 0.287163 | -0.259205 | -0.106656 | 0.412345 | 0.480928 | -0.201399 | -0.613341 | 1 | 1 |
| 194 | 0.071267 | -0.794887 | 0.430112 | 0.394775 | -0.746468 | 0.547360 | -0.308259 | -0.083362 | -0.498384 | 0.517608 | -0.616623 | -0.235300 | 0.616686 | 1 | 1 |
| 195 | -0.430296 | -0.963686 | -0.372758 | 0.074908 | -1.551444 | -2.289278 | -0.996792 | -0.144677 | 0.236885 | 0.775027 | 0.825371 | 0.497421 | -0.293443 | 0 | 1 |
| 196 | -0.295903 | -1.246841 | -0.389881 | 0.186413 | -0.827904 | -1.740339 | -1.375256 | -0.532361 | -0.762533 | -0.840235 | 0.492296 | 0.528822 | -0.126196 | 0 | 1 |
| 197 | 0.479442 | -2.526563 | -2.233640 | -1.311649 | -0.114249 | 0.148620 | 1.711025 | 0.415772 | -2.224830 | -1.067674 | 1.273042 | 0.432741 | -1.020345 | 0 | 1 |
| 198 | -0.472461 | -0.316221 | -2.118686 | -0.390396 | -0.247602 | -0.668064 | -0.201965 | -0.506232 | -0.902628 | -1.005551 | 0.272601 | 0.248004 | -0.241612 | 0 | 1 |
| 199 | -0.006912 | -1.450305 | -0.125795 | -1.995008 | -1.440314 | -0.787148 | 1.230185 | -1.801522 | -0.524097 | -0.296890 | 0.146555 | -0.059935 | -1.742924 | 0 | 1 |
| 200 | -1.045376 | 0.843938 | -1.177992 | -1.026041 | -0.442183 | 0.610204 | 1.906959 | 0.601365 | 1.149266 | 1.040025 | 1.510768 | 1.525596 | 0.008502 | 1 | 1 |
| 201 | -1.160197 | 0.899099 | -1.084036 | -0.745060 | -0.704154 | 0.815025 | 1.572385 | 0.694718 | 1.195502 | 1.357973 | 1.719920 | 1.339687 | 0.346437 | 1 | 1 |
| 202 | 0.846379 | -0.292164 | -2.285100 | -0.590310 | -1.342705 | -0.365748 | -0.736446 | 1.066510 | 1.737515 | 0.368654 | 0.546029 | 0.404179 | -1.048654 | 0 | 1 |
| 203 | 0.365059 | -0.089335 | -0.757014 | -0.150651 | 0.520638 | 0.383469 | 1.170308 | 0.915512 | -1.099501 | -0.480174 | -0.052102 | 0.105614 | -0.010947 | 0 | 1 |
| 204 | 1.230293 | 1.978904 | 0.273786 | -0.096460 | 0.345688 | 0.887889 | -0.409813 | -0.373808 | 0.228442 | 0.696266 | 0.267416 | 0.520666 | 1.007514 | 1 | 1 |
| 205 | -0.042146 | 0.277253 | 0.012741 | -0.226784 | -0.676665 | -0.286922 | 0.799616 | -0.357473 | -0.730921 | 0.222981 | 0.053531 | 0.062325 | 0.479153 | 1 | 1 |
| 206 | -0.520687 | -0.536310 | 0.241607 | -0.135026 | 0.085343 | 1.155527 | 1.524244 | -0.261140 | -0.801648 | 0.645204 | 0.296049 | 1.047521 | 0.226221 | 1 | 1 |
| 207 | -0.563298 | -0.749857 | 0.723480 | 0.034849 | 0.249757 | 0.248513 | 0.065331 | 0.789506 | 0.591196 | 0.578037 | -0.360289 | 1.288016 | 0.495527 | 1 | 1 |
| 208 | 0.959455 | -2.318903 | -0.452832 | -0.892660 | -1.054730 | 1.504668 | -1.691862 | -1.997066 | -1.458509 | 0.390715 | 0.578679 | 0.194999 | -0.104548 | 1 | 1 |
| 209 | -0.388117 | 0.504159 | 0.113071 | 0.183221 | -0.043271 | 0.766480 | -0.092855 | -1.130498 | 0.529058 | -0.199386 | 0.035560 | -0.333834 | 0.078931 | 1 | 1 |
| 210 | 0.756581 | 0.841258 | 1.471905 | -0.630055 | -1.095463 | 3.845135 | -0.131687 | -1.562759 | -0.363967 | -0.026532 | 1.803300 | -4.065727 | -1.024033 | 1 | 1 |
| 211 | 0.113169 | -0.411080 | 0.219643 | 0.091081 | -0.447327 | -0.962500 | -0.624101 | -1.077007 | 0.481174 | -0.271821 | -0.038339 | -0.466845 | 0.707298 | 1 | 1 |
| 212 | 0.854842 | 0.556052 | 1.002658 | 0.968377 | 0.557854 | -0.579130 | -0.854195 | 1.514705 | 1.589294 | 0.351534 | -0.962939 | 0.326626 | 0.297482 | 1 | 1 |
| 213 | 1.087836 | -1.091223 | 1.963519 | -0.088209 | 0.598615 | 0.937138 | -1.807416 | 2.031932 | 0.230433 | 0.443770 | -0.751601 | 0.601455 | 0.233461 | 1 | 1 |
| 214 | -0.595874 | 0.009907 | -0.748990 | -0.070101 | 1.262995 | 1.821824 | 0.850657 | 0.030762 | 0.529494 | -0.624150 | -1.080924 | -0.189073 | 0.819318 | 1 | 1 |
| 215 | -0.002561 | 0.856829 | -0.237776 | -1.449113 | -0.069162 | 0.539513 | 2.378866 | 0.711870 | -1.305789 | -0.778617 | -0.872224 | -2.333142 | -1.454909 | 0 | 1 |
216 rows × 15 columns
In [131]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[131]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b827ab1400>
In [132]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[3]))
Hotel Marrakech¶
ANN¶
In [80]:
X = df_n_ps_std_mfcc[3]
In [81]:
y = df_n_ps[3]['chosen']
In [82]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [83]:
X_train.shape
Out[83]:
(108, 13)
In [84]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [85]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [86]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [140]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (20, 10), 'learning_rate_init': 0.004, 'max_iter': 100}, que permiten obtener un Accuracy de 83.33% y un Kappa del 63.49
Tiempo total: 20.45 minutos
In [87]:
grid.best_params_={'activation': 'relu', 'hidden_layer_sizes': (20, 10), 'learning_rate_init': 0.004, 'max_iter': 100}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [88]:
input_tensor = Input(shape = (n0,))
In [89]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [90]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [91]:
model.summary()
Model: "model_4" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_5 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_12 (Dense) (None, 20) 280 _________________________________________________________________ dense_13 (Dense) (None, 10) 210 _________________________________________________________________ dense_14 (Dense) (None, 1) 11 ================================================================= Total params: 501 Trainable params: 501 Non-trainable params: 0 _________________________________________________________________
In [92]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 108 samples, validate on 36 samples Epoch 1/100 108/108 [==============================] - 0s 1ms/step - loss: 0.8313 - accuracy: 0.4259 - val_loss: 0.8155 - val_accuracy: 0.4444 Epoch 2/100 108/108 [==============================] - 0s 111us/step - loss: 0.7307 - accuracy: 0.4630 - val_loss: 0.7477 - val_accuracy: 0.4444 Epoch 3/100 108/108 [==============================] - 0s 93us/step - loss: 0.6711 - accuracy: 0.5648 - val_loss: 0.7059 - val_accuracy: 0.5000 Epoch 4/100 108/108 [==============================] - 0s 83us/step - loss: 0.6324 - accuracy: 0.6852 - val_loss: 0.6815 - val_accuracy: 0.4722 Epoch 5/100 108/108 [==============================] - 0s 93us/step - loss: 0.6061 - accuracy: 0.6944 - val_loss: 0.6654 - val_accuracy: 0.5556 Epoch 6/100 108/108 [==============================] - 0s 102us/step - loss: 0.5839 - accuracy: 0.7315 - val_loss: 0.6534 - val_accuracy: 0.5833 Epoch 7/100 108/108 [==============================] - 0s 93us/step - loss: 0.5622 - accuracy: 0.7593 - val_loss: 0.6457 - val_accuracy: 0.5833 Epoch 8/100 108/108 [==============================] - 0s 74us/step - loss: 0.5424 - accuracy: 0.7500 - val_loss: 0.6399 - val_accuracy: 0.6389 Epoch 9/100 108/108 [==============================] - 0s 93us/step - loss: 0.5240 - accuracy: 0.7500 - val_loss: 0.6382 - val_accuracy: 0.6389 Epoch 10/100 108/108 [==============================] - 0s 93us/step - loss: 0.5038 - accuracy: 0.7778 - val_loss: 0.6336 - val_accuracy: 0.6389 Epoch 11/100 108/108 [==============================] - 0s 74us/step - loss: 0.4851 - accuracy: 0.7593 - val_loss: 0.6279 - val_accuracy: 0.6667 Epoch 12/100 108/108 [==============================] - 0s 83us/step - loss: 0.4685 - accuracy: 0.7870 - val_loss: 0.6221 - val_accuracy: 0.6667 Epoch 13/100 108/108 [==============================] - 0s 111us/step - loss: 0.4519 - accuracy: 0.7870 - val_loss: 0.6197 - val_accuracy: 0.6944 Epoch 14/100 108/108 [==============================] - 0s 102us/step - loss: 0.4344 - accuracy: 0.7963 - val_loss: 0.6187 - val_accuracy: 0.6944 Epoch 15/100 108/108 [==============================] - 0s 83us/step - loss: 0.4175 - accuracy: 0.8333 - val_loss: 0.6177 - val_accuracy: 0.6944 Epoch 16/100 108/108 [==============================] - 0s 102us/step - loss: 0.4005 - accuracy: 0.8611 - val_loss: 0.6205 - val_accuracy: 0.6667 Epoch 17/100 108/108 [==============================] - 0s 102us/step - loss: 0.3834 - accuracy: 0.8704 - val_loss: 0.6232 - val_accuracy: 0.6667 Epoch 18/100 108/108 [==============================] - 0s 102us/step - loss: 0.3670 - accuracy: 0.8704 - val_loss: 0.6273 - val_accuracy: 0.6944 Epoch 19/100 108/108 [==============================] - 0s 83us/step - loss: 0.3514 - accuracy: 0.8611 - val_loss: 0.6273 - val_accuracy: 0.6944 Epoch 20/100 108/108 [==============================] - 0s 93us/step - loss: 0.3355 - accuracy: 0.8796 - val_loss: 0.6265 - val_accuracy: 0.6389 Epoch 21/100 108/108 [==============================] - 0s 93us/step - loss: 0.3209 - accuracy: 0.8889 - val_loss: 0.6259 - val_accuracy: 0.6389 Epoch 22/100 108/108 [==============================] - 0s 93us/step - loss: 0.3040 - accuracy: 0.8981 - val_loss: 0.6295 - val_accuracy: 0.6389 Epoch 23/100 108/108 [==============================] - 0s 83us/step - loss: 0.2892 - accuracy: 0.9074 - val_loss: 0.6293 - val_accuracy: 0.6389 Epoch 00023: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 24/100 108/108 [==============================] - 0s 102us/step - loss: 0.2760 - accuracy: 0.9074 - val_loss: 0.6278 - val_accuracy: 0.6389 Epoch 25/100 108/108 [==============================] - 0s 102us/step - loss: 0.2682 - accuracy: 0.9074 - val_loss: 0.6278 - val_accuracy: 0.6667 Epoch 26/100 108/108 [==============================] - 0s 74us/step - loss: 0.2611 - accuracy: 0.9167 - val_loss: 0.6304 - val_accuracy: 0.6667 Epoch 27/100 108/108 [==============================] - 0s 102us/step - loss: 0.2541 - accuracy: 0.9167 - val_loss: 0.6343 - val_accuracy: 0.6667 Epoch 28/100 108/108 [==============================] - 0s 93us/step - loss: 0.2467 - accuracy: 0.9167 - val_loss: 0.6322 - val_accuracy: 0.6667 Epoch 29/100 108/108 [==============================] - 0s 102us/step - loss: 0.2392 - accuracy: 0.9259 - val_loss: 0.6337 - val_accuracy: 0.6667 Epoch 30/100 108/108 [==============================] - 0s 83us/step - loss: 0.2318 - accuracy: 0.9352 - val_loss: 0.6348 - val_accuracy: 0.6944 Epoch 31/100 108/108 [==============================] - 0s 93us/step - loss: 0.2250 - accuracy: 0.9444 - val_loss: 0.6407 - val_accuracy: 0.6944 Epoch 32/100 108/108 [==============================] - 0s 102us/step - loss: 0.2176 - accuracy: 0.9444 - val_loss: 0.6457 - val_accuracy: 0.6944 Epoch 33/100 108/108 [==============================] - 0s 93us/step - loss: 0.2109 - accuracy: 0.9444 - val_loss: 0.6501 - val_accuracy: 0.6944 Epoch 00033: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 34/100 108/108 [==============================] - 0s 74us/step - loss: 0.2048 - accuracy: 0.9444 - val_loss: 0.6533 - val_accuracy: 0.6944 Epoch 35/100 108/108 [==============================] - 0s 102us/step - loss: 0.2015 - accuracy: 0.9444 - val_loss: 0.6569 - val_accuracy: 0.6944 Epoch 36/100 108/108 [==============================] - 0s 102us/step - loss: 0.1985 - accuracy: 0.9537 - val_loss: 0.6569 - val_accuracy: 0.6944 Epoch 37/100 108/108 [==============================] - 0s 83us/step - loss: 0.1950 - accuracy: 0.9537 - val_loss: 0.6566 - val_accuracy: 0.6944 Epoch 38/100 108/108 [==============================] - 0s 83us/step - loss: 0.1923 - accuracy: 0.9537 - val_loss: 0.6581 - val_accuracy: 0.6944 Epoch 39/100 108/108 [==============================] - 0s 102us/step - loss: 0.1892 - accuracy: 0.9537 - val_loss: 0.6575 - val_accuracy: 0.6944 Epoch 40/100 108/108 [==============================] - 0s 93us/step - loss: 0.1865 - accuracy: 0.9537 - val_loss: 0.6574 - val_accuracy: 0.6944 Epoch 41/100 108/108 [==============================] - 0s 74us/step - loss: 0.1834 - accuracy: 0.9537 - val_loss: 0.6599 - val_accuracy: 0.6944 Epoch 42/100 108/108 [==============================] - 0s 93us/step - loss: 0.1805 - accuracy: 0.9630 - val_loss: 0.6604 - val_accuracy: 0.6944 Epoch 43/100 108/108 [==============================] - 0s 83us/step - loss: 0.1776 - accuracy: 0.9630 - val_loss: 0.6623 - val_accuracy: 0.6944 Epoch 00043: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 44/100 108/108 [==============================] - 0s 93us/step - loss: 0.1754 - accuracy: 0.9630 - val_loss: 0.6655 - val_accuracy: 0.6944 Epoch 45/100 108/108 [==============================] - 0s 93us/step - loss: 0.1738 - accuracy: 0.9630 - val_loss: 0.6682 - val_accuracy: 0.6944 Epoch 46/100 108/108 [==============================] - 0s 93us/step - loss: 0.1723 - accuracy: 0.9630 - val_loss: 0.6700 - val_accuracy: 0.6944 Epoch 47/100 108/108 [==============================] - 0s 102us/step - loss: 0.1710 - accuracy: 0.9630 - val_loss: 0.6709 - val_accuracy: 0.6944 Epoch 48/100 108/108 [==============================] - 0s 83us/step - loss: 0.1695 - accuracy: 0.9630 - val_loss: 0.6729 - val_accuracy: 0.6944 Epoch 49/100 108/108 [==============================] - 0s 102us/step - loss: 0.1682 - accuracy: 0.9722 - val_loss: 0.6740 - val_accuracy: 0.6944 Epoch 50/100 108/108 [==============================] - 0s 102us/step - loss: 0.1668 - accuracy: 0.9722 - val_loss: 0.6750 - val_accuracy: 0.6944 Epoch 51/100 108/108 [==============================] - 0s 83us/step - loss: 0.1654 - accuracy: 0.9722 - val_loss: 0.6761 - val_accuracy: 0.6944 Epoch 52/100 108/108 [==============================] - 0s 83us/step - loss: 0.1642 - accuracy: 0.9722 - val_loss: 0.6770 - val_accuracy: 0.6944 Epoch 53/100 108/108 [==============================] - 0s 111us/step - loss: 0.1630 - accuracy: 0.9722 - val_loss: 0.6783 - val_accuracy: 0.6944 Epoch 00053: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 54/100 108/108 [==============================] - 0s 111us/step - loss: 0.1618 - accuracy: 0.9722 - val_loss: 0.6791 - val_accuracy: 0.6944 Epoch 55/100 108/108 [==============================] - 0s 130us/step - loss: 0.1612 - accuracy: 0.9722 - val_loss: 0.6804 - val_accuracy: 0.6944 Epoch 56/100 108/108 [==============================] - 0s 111us/step - loss: 0.1606 - accuracy: 0.9722 - val_loss: 0.6819 - val_accuracy: 0.6944 Epoch 57/100 108/108 [==============================] - 0s 83us/step - loss: 0.1600 - accuracy: 0.9722 - val_loss: 0.6833 - val_accuracy: 0.6944 Epoch 58/100 108/108 [==============================] - 0s 93us/step - loss: 0.1594 - accuracy: 0.9722 - val_loss: 0.6844 - val_accuracy: 0.6944 Epoch 59/100 108/108 [==============================] - 0s 102us/step - loss: 0.1587 - accuracy: 0.9722 - val_loss: 0.6850 - val_accuracy: 0.6944 Epoch 60/100 108/108 [==============================] - 0s 102us/step - loss: 0.1580 - accuracy: 0.9722 - val_loss: 0.6856 - val_accuracy: 0.6944 Epoch 61/100 108/108 [==============================] - 0s 111us/step - loss: 0.1574 - accuracy: 0.9722 - val_loss: 0.6854 - val_accuracy: 0.6944 Epoch 62/100 108/108 [==============================] - 0s 111us/step - loss: 0.1567 - accuracy: 0.9722 - val_loss: 0.6852 - val_accuracy: 0.6944 Epoch 63/100 108/108 [==============================] - 0s 93us/step - loss: 0.1561 - accuracy: 0.9722 - val_loss: 0.6849 - val_accuracy: 0.6944 Epoch 00063: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 64/100 108/108 [==============================] - 0s 102us/step - loss: 0.1556 - accuracy: 0.9722 - val_loss: 0.6848 - val_accuracy: 0.6944 Epoch 65/100 108/108 [==============================] - 0s 111us/step - loss: 0.1552 - accuracy: 0.9722 - val_loss: 0.6847 - val_accuracy: 0.6944 Epoch 66/100 108/108 [==============================] - 0s 111us/step - loss: 0.1550 - accuracy: 0.9722 - val_loss: 0.6847 - val_accuracy: 0.6944 Epoch 67/100 108/108 [==============================] - 0s 102us/step - loss: 0.1547 - accuracy: 0.9722 - val_loss: 0.6849 - val_accuracy: 0.6944 Epoch 68/100 108/108 [==============================] - 0s 102us/step - loss: 0.1543 - accuracy: 0.9722 - val_loss: 0.6848 - val_accuracy: 0.6944 Epoch 69/100 108/108 [==============================] - 0s 102us/step - loss: 0.1540 - accuracy: 0.9722 - val_loss: 0.6847 - val_accuracy: 0.6944 Epoch 70/100 108/108 [==============================] - 0s 111us/step - loss: 0.1537 - accuracy: 0.9722 - val_loss: 0.6845 - val_accuracy: 0.6944 Epoch 71/100 108/108 [==============================] - 0s 102us/step - loss: 0.1534 - accuracy: 0.9722 - val_loss: 0.6849 - val_accuracy: 0.6944 Epoch 72/100 108/108 [==============================] - 0s 93us/step - loss: 0.1531 - accuracy: 0.9722 - val_loss: 0.6852 - val_accuracy: 0.6944 Epoch 73/100 108/108 [==============================] - 0s 83us/step - loss: 0.1528 - accuracy: 0.9722 - val_loss: 0.6855 - val_accuracy: 0.6944 Epoch 00073: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 74/100 108/108 [==============================] - 0s 93us/step - loss: 0.1525 - accuracy: 0.9722 - val_loss: 0.6857 - val_accuracy: 0.6944 Epoch 75/100 108/108 [==============================] - 0s 102us/step - loss: 0.1523 - accuracy: 0.9722 - val_loss: 0.6858 - val_accuracy: 0.6944 Epoch 76/100 108/108 [==============================] - 0s 102us/step - loss: 0.1522 - accuracy: 0.9722 - val_loss: 0.6857 - val_accuracy: 0.6944 Epoch 77/100 108/108 [==============================] - 0s 93us/step - loss: 0.1521 - accuracy: 0.9722 - val_loss: 0.6858 - val_accuracy: 0.6944 Epoch 78/100 108/108 [==============================] - 0s 102us/step - loss: 0.1519 - accuracy: 0.9722 - val_loss: 0.6859 - val_accuracy: 0.6944 Epoch 79/100 108/108 [==============================] - 0s 93us/step - loss: 0.1517 - accuracy: 0.9722 - val_loss: 0.6863 - val_accuracy: 0.6944 Epoch 80/100 108/108 [==============================] - 0s 102us/step - loss: 0.1516 - accuracy: 0.9722 - val_loss: 0.6866 - val_accuracy: 0.6944 Epoch 81/100 108/108 [==============================] - 0s 83us/step - loss: 0.1514 - accuracy: 0.9722 - val_loss: 0.6869 - val_accuracy: 0.6944 Epoch 82/100 108/108 [==============================] - 0s 93us/step - loss: 0.1513 - accuracy: 0.9722 - val_loss: 0.6874 - val_accuracy: 0.6944 Epoch 83/100 108/108 [==============================] - 0s 93us/step - loss: 0.1511 - accuracy: 0.9722 - val_loss: 0.6877 - val_accuracy: 0.6944 Epoch 00083: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 84/100 108/108 [==============================] - 0s 83us/step - loss: 0.1510 - accuracy: 0.9722 - val_loss: 0.6879 - val_accuracy: 0.6944 Epoch 85/100 108/108 [==============================] - 0s 83us/step - loss: 0.1509 - accuracy: 0.9722 - val_loss: 0.6880 - val_accuracy: 0.6944 Epoch 86/100 108/108 [==============================] - 0s 93us/step - loss: 0.1508 - accuracy: 0.9722 - val_loss: 0.6881 - val_accuracy: 0.6944 Epoch 87/100 108/108 [==============================] - 0s 83us/step - loss: 0.1508 - accuracy: 0.9722 - val_loss: 0.6882 - val_accuracy: 0.6944 Epoch 88/100 108/108 [==============================] - 0s 74us/step - loss: 0.1507 - accuracy: 0.9722 - val_loss: 0.6883 - val_accuracy: 0.6944 Epoch 89/100 108/108 [==============================] - 0s 102us/step - loss: 0.1506 - accuracy: 0.9722 - val_loss: 0.6883 - val_accuracy: 0.6944 Epoch 90/100 108/108 [==============================] - 0s 93us/step - loss: 0.1505 - accuracy: 0.9722 - val_loss: 0.6884 - val_accuracy: 0.6944 Epoch 91/100 108/108 [==============================] - 0s 83us/step - loss: 0.1505 - accuracy: 0.9722 - val_loss: 0.6884 - val_accuracy: 0.6944 Epoch 92/100 108/108 [==============================] - 0s 111us/step - loss: 0.1504 - accuracy: 0.9722 - val_loss: 0.6886 - val_accuracy: 0.6944 Epoch 93/100 108/108 [==============================] - 0s 93us/step - loss: 0.1503 - accuracy: 0.9722 - val_loss: 0.6888 - val_accuracy: 0.6944 Epoch 00093: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 94/100 108/108 [==============================] - 0s 102us/step - loss: 0.1502 - accuracy: 0.9722 - val_loss: 0.6889 - val_accuracy: 0.6944 Epoch 95/100 108/108 [==============================] - 0s 83us/step - loss: 0.1502 - accuracy: 0.9722 - val_loss: 0.6890 - val_accuracy: 0.6944 Epoch 96/100 108/108 [==============================] - 0s 111us/step - loss: 0.1502 - accuracy: 0.9722 - val_loss: 0.6891 - val_accuracy: 0.6944 Epoch 97/100 108/108 [==============================] - 0s 102us/step - loss: 0.1501 - accuracy: 0.9722 - val_loss: 0.6892 - val_accuracy: 0.6944 Epoch 98/100 108/108 [==============================] - 0s 83us/step - loss: 0.1501 - accuracy: 0.9722 - val_loss: 0.6893 - val_accuracy: 0.6944 Epoch 99/100 108/108 [==============================] - 0s 83us/step - loss: 0.1500 - accuracy: 0.9722 - val_loss: 0.6893 - val_accuracy: 0.6944 Epoch 100/100 108/108 [==============================] - 0s 93us/step - loss: 0.1500 - accuracy: 0.9722 - val_loss: 0.6894 - val_accuracy: 0.6944
In [93]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 100)
In [94]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
36/36 [==============================] - 0s 83us/step test loss: 0.6894227663675944, test accuracy: 0.6944444179534912
In [95]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.7725752508361204
In [96]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.38888888888888884 [[15 8] [ 3 10]]
KMeans¶
In [151]:
X
Out[151]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.221235 | 1.617887 | 0.929874 | -0.231486 | -0.525862 | 1.384826 | 0.709441 | 0.512679 | -2.231286 | -2.278872 | -0.728806 | -2.187766 | -1.206544 |
| 1 | 0.836735 | -0.529605 | -1.268139 | -0.791053 | 0.815880 | -1.992230 | -0.371430 | -0.356669 | 1.323871 | 0.946394 | -1.085097 | 0.673490 | -1.496313 |
| 2 | -0.190995 | 1.202756 | 0.050028 | -2.631154 | 3.701544 | -1.158173 | 0.439586 | 2.317548 | -2.282526 | -1.571775 | -2.541951 | -2.587380 | -2.132445 |
| 3 | 0.521202 | 1.354284 | 1.423683 | -0.634173 | 0.934734 | 0.214772 | -0.349135 | 1.009101 | -2.193012 | -0.301254 | -0.356046 | -0.668937 | -0.421263 |
| 4 | 0.250234 | 1.586078 | -1.791096 | 0.127156 | 1.573000 | 0.288525 | 1.962471 | 1.500627 | 1.352853 | -1.921935 | 0.705405 | -0.230103 | -0.803009 |
| 5 | 0.333238 | -0.983017 | -1.253129 | -0.703445 | -0.390741 | -0.904476 | -0.271254 | 1.452321 | -0.581049 | 0.023331 | -1.113368 | 1.679210 | 1.637860 |
| 6 | 0.124335 | -1.665835 | -2.227171 | -0.835611 | 0.574827 | -2.080147 | -0.569831 | 1.427555 | 0.050593 | 0.046984 | 0.165593 | 2.377458 | 0.856636 |
| 7 | -0.874325 | -0.031806 | 0.246893 | 0.344556 | 0.649466 | 0.670946 | 0.058839 | -0.056305 | -0.189911 | -0.166885 | 0.518138 | -0.045234 | -0.062791 |
| 8 | 0.326792 | -0.610166 | -0.550927 | -0.576489 | -0.918038 | -0.331125 | -0.592815 | 0.924677 | 0.042034 | 0.230600 | -0.251229 | 0.688556 | 1.249983 |
| 9 | -1.469326 | 0.426500 | 2.520170 | 1.045079 | -0.410299 | 0.561905 | 0.502913 | -0.074933 | -0.194040 | -0.642182 | -0.535037 | -0.277243 | 0.886176 |
| 10 | -1.602658 | -1.245330 | -1.650120 | 1.696521 | -0.387907 | -0.033057 | -1.456368 | 0.024357 | 1.001153 | 1.565037 | -0.646889 | 0.239423 | 0.993436 |
| 11 | -1.820295 | -1.526937 | -1.324778 | 2.559842 | -0.126763 | -0.206232 | -1.800200 | 0.194869 | 0.807199 | 0.370292 | -0.668713 | 0.417022 | 1.455716 |
| 12 | -0.764909 | 0.894911 | 0.268560 | -0.209448 | -0.230305 | -0.132543 | 0.157299 | -0.216200 | -0.073587 | 0.425168 | 1.208283 | 0.176103 | -0.227052 |
| 13 | -2.021592 | -0.256925 | 0.107447 | 0.355253 | 1.242070 | -0.097325 | 0.575987 | 0.181357 | -0.173788 | 0.312045 | -1.135063 | -1.669265 | -1.430807 |
| 14 | -0.561508 | 0.034689 | -0.118674 | 0.135424 | -0.766656 | -0.507684 | 0.108448 | 0.723647 | 0.198360 | -0.010792 | 0.011033 | -0.011280 | -1.011492 |
| 15 | 1.081988 | 0.945357 | -0.230748 | 0.314322 | 0.133962 | 0.296502 | 0.325135 | -0.722499 | -0.700028 | -0.027093 | 0.102971 | -0.815814 | 0.889176 |
| 16 | 0.341450 | 3.113983 | 0.419314 | 1.087397 | 2.159626 | 0.542690 | 0.106593 | 0.433048 | -0.130652 | -0.483111 | 0.378798 | 0.475149 | 0.814020 |
| 17 | -0.465680 | 0.048325 | -1.647951 | -1.343412 | 0.783737 | 1.435160 | -0.831385 | 0.665605 | 0.805481 | 1.237871 | 0.437312 | 0.293056 | -0.077970 |
| 18 | -0.518957 | -0.205911 | -0.935801 | -0.519162 | 0.488287 | 0.429763 | 0.957373 | 0.025670 | -0.655265 | -0.541455 | 1.027838 | 0.655340 | 0.929453 |
| 19 | 0.065304 | 0.074045 | 0.004340 | 0.740126 | 0.742134 | 0.291814 | -0.940237 | -0.030565 | 2.291217 | 0.873108 | 0.369910 | 0.824246 | 0.356735 |
| 20 | -0.383757 | 0.437022 | 0.907339 | 0.842096 | 1.161748 | 0.721193 | 0.231956 | -0.806816 | -0.441393 | -0.075681 | 0.273756 | 0.148276 | 0.377982 |
| 21 | 0.558290 | 1.010237 | -0.882410 | -0.313261 | -1.108460 | -0.061251 | 1.439980 | 0.042899 | -0.731331 | -1.580437 | -1.114403 | -1.504838 | -0.119322 |
| 22 | 0.470380 | -0.322318 | -1.191863 | -0.092570 | 0.408703 | -1.017213 | 0.435319 | 0.248928 | -0.094380 | 0.418018 | 0.217435 | -0.516998 | -0.868055 |
| 23 | 0.594195 | 0.466425 | -1.651421 | -1.153236 | -2.163553 | -1.957716 | -0.238416 | 0.695815 | 0.332270 | -0.061240 | -0.338001 | 1.191380 | 0.653576 |
| 24 | -0.294279 | -0.469828 | -0.506281 | 0.553411 | -0.002394 | -1.210177 | -1.635390 | -0.068027 | -0.072194 | -0.646855 | -0.472007 | 0.640983 | 1.465438 |
| 25 | -0.044753 | 0.008070 | 0.124675 | 0.401165 | -1.495260 | -2.095123 | -1.070614 | 0.040115 | -0.592197 | -0.491126 | 0.440431 | 1.007611 | 0.450109 |
| 26 | -0.361795 | -0.336369 | -1.184798 | 0.051223 | 0.460059 | -1.947295 | -2.267630 | 0.341965 | 0.234544 | 0.029052 | 0.004198 | 0.189779 | 1.146805 |
| 27 | 0.842916 | 0.418905 | -0.554491 | 0.388861 | 1.276091 | 0.351522 | 0.411002 | -0.949650 | -0.431041 | 0.317882 | -0.888404 | -0.897541 | -0.085071 |
| 28 | -1.213666 | 1.250188 | -1.066058 | -0.663600 | 1.489036 | 0.477183 | 0.408848 | 0.354581 | -0.567870 | 0.781751 | -0.085926 | 2.017666 | 0.067778 |
| 29 | -1.003139 | 0.015927 | -0.366063 | 0.248303 | 0.256190 | 0.748222 | 1.200549 | 0.305672 | -0.124149 | -0.260150 | -0.065036 | 0.009866 | -0.589000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 114 | 0.145143 | -1.087536 | -0.109574 | 1.182367 | -0.234880 | 0.595609 | -0.167528 | -0.375039 | -0.229571 | -0.013944 | 0.570663 | -0.431994 | 0.150206 |
| 115 | -1.711163 | -0.746315 | -0.531486 | -0.426122 | 0.265388 | 0.755026 | 0.557866 | -0.376462 | 0.447626 | -1.092869 | -1.345461 | -1.913386 | -0.678209 |
| 116 | 0.392290 | 1.069548 | -0.306184 | 0.145336 | -0.217734 | -0.733749 | 0.435935 | 0.749464 | 1.217672 | 0.211105 | 1.231701 | 0.188785 | 0.434754 |
| 117 | 0.935840 | 0.716917 | 0.007000 | 0.627264 | 0.191403 | -0.333462 | 0.823069 | 0.058921 | 0.011376 | 0.852634 | 0.522502 | 0.556864 | 0.607905 |
| 118 | 0.197008 | 0.584804 | -0.002674 | 0.755814 | 0.284618 | -1.252463 | 0.407862 | 0.894361 | 0.769537 | 0.220578 | 0.749417 | 0.100243 | -0.151070 |
| 119 | 2.405490 | -0.155599 | 0.811293 | 1.347936 | 0.825563 | 0.461353 | 0.894124 | 0.344794 | 1.893152 | 2.258728 | 0.129193 | -0.243084 | -0.058467 |
| 120 | 0.715503 | 0.871839 | 0.292274 | 0.271363 | -0.696526 | 0.777498 | 2.464116 | -0.386285 | 1.685524 | 1.576706 | 0.185429 | 0.140475 | 0.421924 |
| 121 | 2.417611 | 0.241031 | 1.233666 | 0.460035 | 0.057428 | 0.040149 | 3.378156 | 0.381120 | -0.121501 | -0.324116 | -0.176822 | 1.227364 | 0.614724 |
| 122 | -0.318929 | -0.810559 | -0.844588 | 0.201697 | -0.001562 | 0.245109 | 0.080448 | -0.549388 | 1.103198 | 0.291492 | 0.110564 | -0.673124 | -1.460988 |
| 123 | 0.103167 | -0.475246 | 0.116339 | -0.525138 | -0.644659 | 0.818829 | 0.559379 | -0.142633 | -0.157352 | -0.469393 | 0.012773 | -0.547972 | -0.137509 |
| 124 | -0.094617 | -0.605067 | -0.742357 | 0.268847 | 0.524751 | 0.458324 | 0.276908 | -0.302065 | 0.138633 | 0.644511 | 0.740681 | -0.411278 | -0.888780 |
| 125 | -1.595914 | -0.979410 | -0.416646 | 0.594734 | -0.542828 | 0.792587 | -1.964236 | -0.008409 | -0.175168 | 0.129402 | -0.381751 | 0.557976 | 0.657622 |
| 126 | -1.902096 | -0.148093 | -0.337830 | -0.273661 | -0.493964 | -0.088475 | -0.220561 | -0.292045 | -0.103285 | 0.166066 | 0.662068 | 0.766244 | 0.425791 |
| 127 | -1.605568 | -0.554121 | 0.460876 | 1.019950 | -0.718272 | 1.457022 | -1.697711 | 0.811490 | -0.088705 | -0.660478 | -0.973868 | 0.581804 | 0.414185 |
| 128 | 0.566475 | -0.266552 | -0.411015 | -1.148969 | 0.130007 | -0.378875 | -0.472183 | -1.264777 | 0.333529 | -0.198779 | 0.021059 | -0.888067 | 0.082910 |
| 129 | 0.526340 | -0.028603 | -0.164620 | -1.269989 | 0.446406 | -0.087302 | -0.000812 | -1.253016 | -0.043573 | -0.441529 | -0.260177 | -1.155496 | 0.048018 |
| 130 | -0.506596 | -0.917555 | 0.022348 | -2.928480 | -0.397810 | 1.517764 | -2.223896 | -3.454969 | -0.250982 | 2.385778 | 2.673937 | 0.852384 | 0.503967 |
| 131 | 0.241077 | 0.331430 | -0.183360 | 0.450777 | 1.050602 | 1.030703 | -0.632082 | -0.613020 | -0.605923 | -0.445150 | 0.601823 | -0.068405 | -0.239919 |
| 132 | 0.384872 | 0.341763 | 0.211728 | -0.437357 | 1.099689 | 2.173078 | -0.523080 | 0.221203 | 1.791178 | -0.924414 | 0.882953 | -0.116331 | -0.105512 |
| 133 | 0.926004 | -0.715730 | 0.121647 | -0.799985 | -1.043066 | -1.309977 | 0.089931 | 0.501863 | 1.200490 | 0.946479 | 0.949711 | 0.122093 | -0.106795 |
| 134 | 0.108168 | -0.201129 | -0.571770 | -0.710757 | -0.229892 | -0.308178 | 1.035292 | 0.063963 | -0.410105 | 0.116893 | 1.018084 | -0.213796 | 0.002336 |
| 135 | -0.243379 | 0.039038 | -0.185527 | 0.341086 | 0.733620 | -1.255089 | 0.422881 | 0.253117 | 1.161221 | 0.252360 | 0.377116 | -0.444191 | 0.208913 |
| 136 | 1.306831 | 0.220282 | -0.386785 | -1.153056 | 1.946157 | 1.302925 | -1.248915 | -0.883391 | -0.265715 | 1.982123 | 1.319726 | 0.442799 | 1.500702 |
| 137 | 1.247354 | 0.605285 | -0.077094 | 0.407727 | 1.841346 | -0.789475 | -1.501921 | 0.627200 | 1.160569 | -0.001277 | -0.516474 | 0.653115 | 1.248304 |
| 138 | 0.708935 | -1.127949 | 0.861675 | -1.147026 | -0.241837 | -1.657997 | -0.127631 | 2.115046 | -0.340085 | 1.759678 | -0.965027 | 1.205391 | 0.879613 |
| 139 | 0.708839 | -0.956820 | -1.350334 | -1.562796 | 0.707567 | -0.389784 | -2.197459 | 2.741627 | 1.474984 | -0.351995 | 1.728271 | 1.249063 | 0.769635 |
| 140 | 1.483186 | 0.682555 | -0.612419 | 0.474508 | 0.910933 | 1.248228 | 0.176444 | 0.490360 | 1.208370 | -0.434267 | -0.260485 | -0.319060 | 0.067114 |
| 141 | 0.201126 | 0.561986 | 0.031425 | 0.091477 | 0.565265 | 1.039870 | 0.116405 | -0.484695 | -1.094180 | -1.320674 | 0.773891 | 0.702095 | 0.386850 |
| 142 | 0.003924 | -1.301791 | -0.501001 | 1.193249 | -0.495635 | 0.002788 | -1.372557 | -0.559755 | -0.221626 | -0.659689 | 0.093759 | -0.130319 | 0.035051 |
| 143 | -0.055580 | -0.659181 | -1.244063 | -0.214578 | 0.114523 | -0.633686 | -0.516044 | 0.580491 | 1.035424 | 0.468185 | 0.471840 | 1.199764 | 0.150708 |
144 rows × 13 columns
In [152]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[152]:
[1872.0, 1679.2968315149515, 1550.7545737855435, 1446.6172108036508, 1373.1093324372473, 1322.1800157528337, 1260.9432250958696, 1220.6940995550194, 1171.7851814633127, 1127.850987607133, 1106.05463123734, 1024.1042711193513, 1004.7449935321633, 976.292238260163]
In [153]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[153]:
[<matplotlib.lines.Line2D at 0x1b82940b0b8>]
K=3
In [154]:
kmeans_mfcc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[154]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [155]:
kmeans_mfcc.labels_
Out[155]:
array([2, 0, 0, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 0, 0, 2, 2, 0, 1, 2, 2, 0,
0, 1, 1, 1, 1, 2, 1, 0, 1, 0, 0, 2, 0, 1, 1, 0, 2, 1, 0, 0, 0, 1,
1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2, 2, 1, 0, 2, 2, 0, 2, 0, 2, 1,
0, 0, 1, 1, 1, 1, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 2, 1, 2,
0, 2, 2, 2, 1, 2, 1, 2, 2, 0, 1, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0, 2,
0, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 1, 1, 0, 0, 1, 2,
2, 0, 0, 0, 2, 2, 1, 1, 2, 2, 1, 1])
In [156]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[156]:
array([2, 0, 0, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 0, 0, 2, 2, 0, 1, 2, 2, 0,
0, 1, 1, 1, 1, 2, 1, 0, 1, 0, 0, 2, 0, 1, 1, 0, 2, 1, 0, 0, 0, 1,
1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2, 2, 1, 0, 2, 2, 0, 2, 0, 2, 1,
0, 0, 1, 1, 1, 1, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 2, 1, 2,
0, 2, 2, 2, 1, 2, 1, 2, 2, 0, 1, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0, 2,
0, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 1, 1, 0, 0, 1, 2,
2, 0, 0, 0, 2, 2, 1, 1, 2, 2, 1, 1])
In [157]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [158]:
X
Out[158]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.221235 | 1.617887 | 0.929874 | -0.231486 | -0.525862 | 1.384826 | 0.709441 | 0.512679 | -2.231286 | -2.278872 | -0.728806 | -2.187766 | -1.206544 | 2 | 0 |
| 1 | 0.836735 | -0.529605 | -1.268139 | -0.791053 | 0.815880 | -1.992230 | -0.371430 | -0.356669 | 1.323871 | 0.946394 | -1.085097 | 0.673490 | -1.496313 | 0 | 0 |
| 2 | -0.190995 | 1.202756 | 0.050028 | -2.631154 | 3.701544 | -1.158173 | 0.439586 | 2.317548 | -2.282526 | -1.571775 | -2.541951 | -2.587380 | -2.132445 | 0 | 0 |
| 3 | 0.521202 | 1.354284 | 1.423683 | -0.634173 | 0.934734 | 0.214772 | -0.349135 | 1.009101 | -2.193012 | -0.301254 | -0.356046 | -0.668937 | -0.421263 | 2 | 0 |
| 4 | 0.250234 | 1.586078 | -1.791096 | 0.127156 | 1.573000 | 0.288525 | 1.962471 | 1.500627 | 1.352853 | -1.921935 | 0.705405 | -0.230103 | -0.803009 | 2 | 0 |
| 5 | 0.333238 | -0.983017 | -1.253129 | -0.703445 | -0.390741 | -0.904476 | -0.271254 | 1.452321 | -0.581049 | 0.023331 | -1.113368 | 1.679210 | 1.637860 | 1 | 0 |
| 6 | 0.124335 | -1.665835 | -2.227171 | -0.835611 | 0.574827 | -2.080147 | -0.569831 | 1.427555 | 0.050593 | 0.046984 | 0.165593 | 2.377458 | 0.856636 | 1 | 0 |
| 7 | -0.874325 | -0.031806 | 0.246893 | 0.344556 | 0.649466 | 0.670946 | 0.058839 | -0.056305 | -0.189911 | -0.166885 | 0.518138 | -0.045234 | -0.062791 | 2 | 0 |
| 8 | 0.326792 | -0.610166 | -0.550927 | -0.576489 | -0.918038 | -0.331125 | -0.592815 | 0.924677 | 0.042034 | 0.230600 | -0.251229 | 0.688556 | 1.249983 | 1 | 0 |
| 9 | -1.469326 | 0.426500 | 2.520170 | 1.045079 | -0.410299 | 0.561905 | 0.502913 | -0.074933 | -0.194040 | -0.642182 | -0.535037 | -0.277243 | 0.886176 | 2 | 0 |
| 10 | -1.602658 | -1.245330 | -1.650120 | 1.696521 | -0.387907 | -0.033057 | -1.456368 | 0.024357 | 1.001153 | 1.565037 | -0.646889 | 0.239423 | 0.993436 | 1 | 0 |
| 11 | -1.820295 | -1.526937 | -1.324778 | 2.559842 | -0.126763 | -0.206232 | -1.800200 | 0.194869 | 0.807199 | 0.370292 | -0.668713 | 0.417022 | 1.455716 | 1 | 0 |
| 12 | -0.764909 | 0.894911 | 0.268560 | -0.209448 | -0.230305 | -0.132543 | 0.157299 | -0.216200 | -0.073587 | 0.425168 | 1.208283 | 0.176103 | -0.227052 | 1 | 0 |
| 13 | -2.021592 | -0.256925 | 0.107447 | 0.355253 | 1.242070 | -0.097325 | 0.575987 | 0.181357 | -0.173788 | 0.312045 | -1.135063 | -1.669265 | -1.430807 | 0 | 0 |
| 14 | -0.561508 | 0.034689 | -0.118674 | 0.135424 | -0.766656 | -0.507684 | 0.108448 | 0.723647 | 0.198360 | -0.010792 | 0.011033 | -0.011280 | -1.011492 | 0 | 0 |
| 15 | 1.081988 | 0.945357 | -0.230748 | 0.314322 | 0.133962 | 0.296502 | 0.325135 | -0.722499 | -0.700028 | -0.027093 | 0.102971 | -0.815814 | 0.889176 | 2 | 0 |
| 16 | 0.341450 | 3.113983 | 0.419314 | 1.087397 | 2.159626 | 0.542690 | 0.106593 | 0.433048 | -0.130652 | -0.483111 | 0.378798 | 0.475149 | 0.814020 | 2 | 0 |
| 17 | -0.465680 | 0.048325 | -1.647951 | -1.343412 | 0.783737 | 1.435160 | -0.831385 | 0.665605 | 0.805481 | 1.237871 | 0.437312 | 0.293056 | -0.077970 | 0 | 0 |
| 18 | -0.518957 | -0.205911 | -0.935801 | -0.519162 | 0.488287 | 0.429763 | 0.957373 | 0.025670 | -0.655265 | -0.541455 | 1.027838 | 0.655340 | 0.929453 | 1 | 0 |
| 19 | 0.065304 | 0.074045 | 0.004340 | 0.740126 | 0.742134 | 0.291814 | -0.940237 | -0.030565 | 2.291217 | 0.873108 | 0.369910 | 0.824246 | 0.356735 | 2 | 0 |
| 20 | -0.383757 | 0.437022 | 0.907339 | 0.842096 | 1.161748 | 0.721193 | 0.231956 | -0.806816 | -0.441393 | -0.075681 | 0.273756 | 0.148276 | 0.377982 | 2 | 0 |
| 21 | 0.558290 | 1.010237 | -0.882410 | -0.313261 | -1.108460 | -0.061251 | 1.439980 | 0.042899 | -0.731331 | -1.580437 | -1.114403 | -1.504838 | -0.119322 | 0 | 0 |
| 22 | 0.470380 | -0.322318 | -1.191863 | -0.092570 | 0.408703 | -1.017213 | 0.435319 | 0.248928 | -0.094380 | 0.418018 | 0.217435 | -0.516998 | -0.868055 | 0 | 0 |
| 23 | 0.594195 | 0.466425 | -1.651421 | -1.153236 | -2.163553 | -1.957716 | -0.238416 | 0.695815 | 0.332270 | -0.061240 | -0.338001 | 1.191380 | 0.653576 | 1 | 0 |
| 24 | -0.294279 | -0.469828 | -0.506281 | 0.553411 | -0.002394 | -1.210177 | -1.635390 | -0.068027 | -0.072194 | -0.646855 | -0.472007 | 0.640983 | 1.465438 | 1 | 0 |
| 25 | -0.044753 | 0.008070 | 0.124675 | 0.401165 | -1.495260 | -2.095123 | -1.070614 | 0.040115 | -0.592197 | -0.491126 | 0.440431 | 1.007611 | 0.450109 | 1 | 0 |
| 26 | -0.361795 | -0.336369 | -1.184798 | 0.051223 | 0.460059 | -1.947295 | -2.267630 | 0.341965 | 0.234544 | 0.029052 | 0.004198 | 0.189779 | 1.146805 | 1 | 0 |
| 27 | 0.842916 | 0.418905 | -0.554491 | 0.388861 | 1.276091 | 0.351522 | 0.411002 | -0.949650 | -0.431041 | 0.317882 | -0.888404 | -0.897541 | -0.085071 | 2 | 0 |
| 28 | -1.213666 | 1.250188 | -1.066058 | -0.663600 | 1.489036 | 0.477183 | 0.408848 | 0.354581 | -0.567870 | 0.781751 | -0.085926 | 2.017666 | 0.067778 | 1 | 0 |
| 29 | -1.003139 | 0.015927 | -0.366063 | 0.248303 | 0.256190 | 0.748222 | 1.200549 | 0.305672 | -0.124149 | -0.260150 | -0.065036 | 0.009866 | -0.589000 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 114 | 0.145143 | -1.087536 | -0.109574 | 1.182367 | -0.234880 | 0.595609 | -0.167528 | -0.375039 | -0.229571 | -0.013944 | 0.570663 | -0.431994 | 0.150206 | 2 | 1 |
| 115 | -1.711163 | -0.746315 | -0.531486 | -0.426122 | 0.265388 | 0.755026 | 0.557866 | -0.376462 | 0.447626 | -1.092869 | -1.345461 | -1.913386 | -0.678209 | 0 | 1 |
| 116 | 0.392290 | 1.069548 | -0.306184 | 0.145336 | -0.217734 | -0.733749 | 0.435935 | 0.749464 | 1.217672 | 0.211105 | 1.231701 | 0.188785 | 0.434754 | 2 | 1 |
| 117 | 0.935840 | 0.716917 | 0.007000 | 0.627264 | 0.191403 | -0.333462 | 0.823069 | 0.058921 | 0.011376 | 0.852634 | 0.522502 | 0.556864 | 0.607905 | 2 | 1 |
| 118 | 0.197008 | 0.584804 | -0.002674 | 0.755814 | 0.284618 | -1.252463 | 0.407862 | 0.894361 | 0.769537 | 0.220578 | 0.749417 | 0.100243 | -0.151070 | 2 | 1 |
| 119 | 2.405490 | -0.155599 | 0.811293 | 1.347936 | 0.825563 | 0.461353 | 0.894124 | 0.344794 | 1.893152 | 2.258728 | 0.129193 | -0.243084 | -0.058467 | 2 | 1 |
| 120 | 0.715503 | 0.871839 | 0.292274 | 0.271363 | -0.696526 | 0.777498 | 2.464116 | -0.386285 | 1.685524 | 1.576706 | 0.185429 | 0.140475 | 0.421924 | 2 | 1 |
| 121 | 2.417611 | 0.241031 | 1.233666 | 0.460035 | 0.057428 | 0.040149 | 3.378156 | 0.381120 | -0.121501 | -0.324116 | -0.176822 | 1.227364 | 0.614724 | 2 | 1 |
| 122 | -0.318929 | -0.810559 | -0.844588 | 0.201697 | -0.001562 | 0.245109 | 0.080448 | -0.549388 | 1.103198 | 0.291492 | 0.110564 | -0.673124 | -1.460988 | 0 | 1 |
| 123 | 0.103167 | -0.475246 | 0.116339 | -0.525138 | -0.644659 | 0.818829 | 0.559379 | -0.142633 | -0.157352 | -0.469393 | 0.012773 | -0.547972 | -0.137509 | 0 | 1 |
| 124 | -0.094617 | -0.605067 | -0.742357 | 0.268847 | 0.524751 | 0.458324 | 0.276908 | -0.302065 | 0.138633 | 0.644511 | 0.740681 | -0.411278 | -0.888780 | 0 | 1 |
| 125 | -1.595914 | -0.979410 | -0.416646 | 0.594734 | -0.542828 | 0.792587 | -1.964236 | -0.008409 | -0.175168 | 0.129402 | -0.381751 | 0.557976 | 0.657622 | 1 | 1 |
| 126 | -1.902096 | -0.148093 | -0.337830 | -0.273661 | -0.493964 | -0.088475 | -0.220561 | -0.292045 | -0.103285 | 0.166066 | 0.662068 | 0.766244 | 0.425791 | 1 | 1 |
| 127 | -1.605568 | -0.554121 | 0.460876 | 1.019950 | -0.718272 | 1.457022 | -1.697711 | 0.811490 | -0.088705 | -0.660478 | -0.973868 | 0.581804 | 0.414185 | 1 | 1 |
| 128 | 0.566475 | -0.266552 | -0.411015 | -1.148969 | 0.130007 | -0.378875 | -0.472183 | -1.264777 | 0.333529 | -0.198779 | 0.021059 | -0.888067 | 0.082910 | 0 | 1 |
| 129 | 0.526340 | -0.028603 | -0.164620 | -1.269989 | 0.446406 | -0.087302 | -0.000812 | -1.253016 | -0.043573 | -0.441529 | -0.260177 | -1.155496 | 0.048018 | 0 | 1 |
| 130 | -0.506596 | -0.917555 | 0.022348 | -2.928480 | -0.397810 | 1.517764 | -2.223896 | -3.454969 | -0.250982 | 2.385778 | 2.673937 | 0.852384 | 0.503967 | 1 | 1 |
| 131 | 0.241077 | 0.331430 | -0.183360 | 0.450777 | 1.050602 | 1.030703 | -0.632082 | -0.613020 | -0.605923 | -0.445150 | 0.601823 | -0.068405 | -0.239919 | 2 | 1 |
| 132 | 0.384872 | 0.341763 | 0.211728 | -0.437357 | 1.099689 | 2.173078 | -0.523080 | 0.221203 | 1.791178 | -0.924414 | 0.882953 | -0.116331 | -0.105512 | 2 | 1 |
| 133 | 0.926004 | -0.715730 | 0.121647 | -0.799985 | -1.043066 | -1.309977 | 0.089931 | 0.501863 | 1.200490 | 0.946479 | 0.949711 | 0.122093 | -0.106795 | 0 | 1 |
| 134 | 0.108168 | -0.201129 | -0.571770 | -0.710757 | -0.229892 | -0.308178 | 1.035292 | 0.063963 | -0.410105 | 0.116893 | 1.018084 | -0.213796 | 0.002336 | 0 | 1 |
| 135 | -0.243379 | 0.039038 | -0.185527 | 0.341086 | 0.733620 | -1.255089 | 0.422881 | 0.253117 | 1.161221 | 0.252360 | 0.377116 | -0.444191 | 0.208913 | 0 | 1 |
| 136 | 1.306831 | 0.220282 | -0.386785 | -1.153056 | 1.946157 | 1.302925 | -1.248915 | -0.883391 | -0.265715 | 1.982123 | 1.319726 | 0.442799 | 1.500702 | 2 | 1 |
| 137 | 1.247354 | 0.605285 | -0.077094 | 0.407727 | 1.841346 | -0.789475 | -1.501921 | 0.627200 | 1.160569 | -0.001277 | -0.516474 | 0.653115 | 1.248304 | 2 | 1 |
| 138 | 0.708935 | -1.127949 | 0.861675 | -1.147026 | -0.241837 | -1.657997 | -0.127631 | 2.115046 | -0.340085 | 1.759678 | -0.965027 | 1.205391 | 0.879613 | 1 | 1 |
| 139 | 0.708839 | -0.956820 | -1.350334 | -1.562796 | 0.707567 | -0.389784 | -2.197459 | 2.741627 | 1.474984 | -0.351995 | 1.728271 | 1.249063 | 0.769635 | 1 | 1 |
| 140 | 1.483186 | 0.682555 | -0.612419 | 0.474508 | 0.910933 | 1.248228 | 0.176444 | 0.490360 | 1.208370 | -0.434267 | -0.260485 | -0.319060 | 0.067114 | 2 | 1 |
| 141 | 0.201126 | 0.561986 | 0.031425 | 0.091477 | 0.565265 | 1.039870 | 0.116405 | -0.484695 | -1.094180 | -1.320674 | 0.773891 | 0.702095 | 0.386850 | 2 | 1 |
| 142 | 0.003924 | -1.301791 | -0.501001 | 1.193249 | -0.495635 | 0.002788 | -1.372557 | -0.559755 | -0.221626 | -0.659689 | 0.093759 | -0.130319 | 0.035051 | 1 | 1 |
| 143 | -0.055580 | -0.659181 | -1.244063 | -0.214578 | 0.114523 | -0.633686 | -0.516044 | 0.580491 | 1.035424 | 0.468185 | 0.471840 | 1.199764 | 0.150708 | 1 | 1 |
144 rows × 15 columns
In [159]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[159]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b8277fe668>
In [160]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[4]))
Specialized¶
ANN¶
In [97]:
X = df_n_ps_std_mfcc[4]
In [98]:
y = df_n_ps[4]['chosen']
In [99]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [100]:
X_train.shape
Out[100]:
(164, 13)
In [101]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [102]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [103]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [168]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.008, 'max_iter': 300}, que permiten obtener un Accuracy de 68.29% y un Kappa del 35.96
Tiempo total: 24.73 minutos
In [104]:
grid.best_params_={'activation': 'tanh', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.008, 'max_iter': 300}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [105]:
input_tensor = Input(shape = (n0,))
In [106]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [107]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [108]:
model.summary()
Model: "model_5" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_6 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_15 (Dense) (None, 30) 420 _________________________________________________________________ dense_16 (Dense) (None, 1) 31 ================================================================= Total params: 451 Trainable params: 451 Non-trainable params: 0 _________________________________________________________________
In [109]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 164 samples, validate on 55 samples Epoch 1/300 164/164 [==============================] - 0s 1ms/step - loss: 0.7239 - accuracy: 0.4939 - val_loss: 0.7181 - val_accuracy: 0.4727 Epoch 2/300 164/164 [==============================] - 0s 73us/step - loss: 0.6690 - accuracy: 0.6159 - val_loss: 0.6907 - val_accuracy: 0.5091 Epoch 3/300 164/164 [==============================] - 0s 73us/step - loss: 0.6550 - accuracy: 0.6341 - val_loss: 0.6686 - val_accuracy: 0.5636 Epoch 4/300 164/164 [==============================] - 0s 73us/step - loss: 0.6522 - accuracy: 0.6646 - val_loss: 0.6564 - val_accuracy: 0.5818 Epoch 5/300 164/164 [==============================] - 0s 73us/step - loss: 0.6453 - accuracy: 0.6951 - val_loss: 0.6496 - val_accuracy: 0.5636 Epoch 6/300 164/164 [==============================] - 0s 61us/step - loss: 0.6334 - accuracy: 0.6829 - val_loss: 0.6514 - val_accuracy: 0.5818 Epoch 7/300 164/164 [==============================] - 0s 73us/step - loss: 0.6312 - accuracy: 0.6951 - val_loss: 0.6566 - val_accuracy: 0.5818 Epoch 8/300 164/164 [==============================] - 0s 73us/step - loss: 0.6219 - accuracy: 0.6951 - val_loss: 0.6593 - val_accuracy: 0.5455 Epoch 9/300 164/164 [==============================] - 0s 73us/step - loss: 0.6101 - accuracy: 0.6951 - val_loss: 0.6743 - val_accuracy: 0.5818 Epoch 10/300 164/164 [==============================] - 0s 79us/step - loss: 0.6074 - accuracy: 0.6646 - val_loss: 0.6942 - val_accuracy: 0.5455 Epoch 11/300 164/164 [==============================] - 0s 67us/step - loss: 0.5983 - accuracy: 0.6768 - val_loss: 0.6874 - val_accuracy: 0.5273 Epoch 12/300 164/164 [==============================] - 0s 73us/step - loss: 0.5875 - accuracy: 0.7195 - val_loss: 0.6730 - val_accuracy: 0.5455 Epoch 13/300 164/164 [==============================] - 0s 73us/step - loss: 0.5814 - accuracy: 0.7073 - val_loss: 0.6623 - val_accuracy: 0.5636 Epoch 14/300 164/164 [==============================] - 0s 79us/step - loss: 0.5710 - accuracy: 0.7134 - val_loss: 0.6570 - val_accuracy: 0.5636 Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.004000000189989805. Epoch 15/300 164/164 [==============================] - 0s 73us/step - loss: 0.5651 - accuracy: 0.7012 - val_loss: 0.6549 - val_accuracy: 0.5818 Epoch 16/300 164/164 [==============================] - ETA: 0s - loss: 0.5508 - accuracy: 0.78 - 0s 67us/step - loss: 0.5600 - accuracy: 0.7378 - val_loss: 0.6584 - val_accuracy: 0.6000 Epoch 17/300 164/164 [==============================] - 0s 67us/step - loss: 0.5555 - accuracy: 0.7317 - val_loss: 0.6597 - val_accuracy: 0.6182 Epoch 18/300 164/164 [==============================] - 0s 67us/step - loss: 0.5514 - accuracy: 0.7622 - val_loss: 0.6634 - val_accuracy: 0.6000 Epoch 19/300 164/164 [==============================] - 0s 79us/step - loss: 0.5490 - accuracy: 0.7744 - val_loss: 0.6709 - val_accuracy: 0.5273 Epoch 20/300 164/164 [==============================] - 0s 73us/step - loss: 0.5431 - accuracy: 0.7683 - val_loss: 0.6761 - val_accuracy: 0.5273 Epoch 21/300 164/164 [==============================] - 0s 79us/step - loss: 0.5414 - accuracy: 0.7622 - val_loss: 0.6769 - val_accuracy: 0.5273 Epoch 22/300 164/164 [==============================] - 0s 73us/step - loss: 0.5387 - accuracy: 0.7561 - val_loss: 0.6754 - val_accuracy: 0.5273 Epoch 23/300 164/164 [==============================] - 0s 79us/step - loss: 0.5317 - accuracy: 0.7622 - val_loss: 0.6715 - val_accuracy: 0.5455 Epoch 24/300 164/164 [==============================] - 0s 67us/step - loss: 0.5278 - accuracy: 0.7561 - val_loss: 0.6652 - val_accuracy: 0.5636 Epoch 25/300 164/164 [==============================] - 0s 67us/step - loss: 0.5216 - accuracy: 0.7805 - val_loss: 0.6646 - val_accuracy: 0.6000 Epoch 26/300 164/164 [==============================] - 0s 73us/step - loss: 0.5176 - accuracy: 0.7988 - val_loss: 0.6656 - val_accuracy: 0.6182 Epoch 27/300 164/164 [==============================] - 0s 79us/step - loss: 0.5137 - accuracy: 0.8049 - val_loss: 0.6732 - val_accuracy: 0.6182 Epoch 00027: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 28/300 164/164 [==============================] - 0s 79us/step - loss: 0.5110 - accuracy: 0.8110 - val_loss: 0.6759 - val_accuracy: 0.5818 Epoch 29/300 164/164 [==============================] - 0s 79us/step - loss: 0.5085 - accuracy: 0.8110 - val_loss: 0.6713 - val_accuracy: 0.5636 Epoch 30/300 164/164 [==============================] - 0s 79us/step - loss: 0.5073 - accuracy: 0.7988 - val_loss: 0.6656 - val_accuracy: 0.5818 Epoch 31/300 164/164 [==============================] - 0s 67us/step - loss: 0.5058 - accuracy: 0.7988 - val_loss: 0.6638 - val_accuracy: 0.5818 Epoch 32/300 164/164 [==============================] - 0s 61us/step - loss: 0.5030 - accuracy: 0.8171 - val_loss: 0.6598 - val_accuracy: 0.5818 Epoch 33/300 164/164 [==============================] - 0s 73us/step - loss: 0.5001 - accuracy: 0.8171 - val_loss: 0.6587 - val_accuracy: 0.6000 Epoch 34/300 164/164 [==============================] - 0s 85us/step - loss: 0.4975 - accuracy: 0.8171 - val_loss: 0.6580 - val_accuracy: 0.6000 Epoch 35/300 164/164 [==============================] - 0s 85us/step - loss: 0.4947 - accuracy: 0.8110 - val_loss: 0.6555 - val_accuracy: 0.6182 Epoch 36/300 164/164 [==============================] - 0s 85us/step - loss: 0.4924 - accuracy: 0.8232 - val_loss: 0.6522 - val_accuracy: 0.6182 Epoch 37/300 164/164 [==============================] - 0s 79us/step - loss: 0.4896 - accuracy: 0.8232 - val_loss: 0.6506 - val_accuracy: 0.6364 Epoch 38/300 164/164 [==============================] - 0s 73us/step - loss: 0.4873 - accuracy: 0.8232 - val_loss: 0.6510 - val_accuracy: 0.6364 Epoch 39/300 164/164 [==============================] - 0s 67us/step - loss: 0.4845 - accuracy: 0.8171 - val_loss: 0.6480 - val_accuracy: 0.6364 Epoch 40/300 164/164 [==============================] - 0s 73us/step - loss: 0.4807 - accuracy: 0.8232 - val_loss: 0.6458 - val_accuracy: 0.6364 Epoch 41/300 164/164 [==============================] - 0s 73us/step - loss: 0.4782 - accuracy: 0.8354 - val_loss: 0.6433 - val_accuracy: 0.6364 Epoch 42/300 164/164 [==============================] - 0s 79us/step - loss: 0.4758 - accuracy: 0.8354 - val_loss: 0.6435 - val_accuracy: 0.6364 Epoch 43/300 164/164 [==============================] - 0s 104us/step - loss: 0.4742 - accuracy: 0.8354 - val_loss: 0.6458 - val_accuracy: 0.6364 Epoch 44/300 164/164 [==============================] - 0s 91us/step - loss: 0.4713 - accuracy: 0.8415 - val_loss: 0.6430 - val_accuracy: 0.6364 Epoch 45/300 164/164 [==============================] - 0s 79us/step - loss: 0.4686 - accuracy: 0.8476 - val_loss: 0.6421 - val_accuracy: 0.6182 Epoch 46/300 164/164 [==============================] - 0s 79us/step - loss: 0.4657 - accuracy: 0.8476 - val_loss: 0.6387 - val_accuracy: 0.6182 Epoch 47/300 164/164 [==============================] - 0s 67us/step - loss: 0.4637 - accuracy: 0.8537 - val_loss: 0.6390 - val_accuracy: 0.6182 Epoch 00047: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 48/300 164/164 [==============================] - 0s 61us/step - loss: 0.4612 - accuracy: 0.8537 - val_loss: 0.6392 - val_accuracy: 0.6182 Epoch 49/300 164/164 [==============================] - 0s 67us/step - loss: 0.4601 - accuracy: 0.8537 - val_loss: 0.6410 - val_accuracy: 0.6182 Epoch 50/300 164/164 [==============================] - 0s 79us/step - loss: 0.4588 - accuracy: 0.8476 - val_loss: 0.6427 - val_accuracy: 0.6182 Epoch 51/300 164/164 [==============================] - 0s 79us/step - loss: 0.4577 - accuracy: 0.8415 - val_loss: 0.6431 - val_accuracy: 0.6182 Epoch 52/300 164/164 [==============================] - 0s 73us/step - loss: 0.4562 - accuracy: 0.8537 - val_loss: 0.6440 - val_accuracy: 0.6182 Epoch 53/300 164/164 [==============================] - 0s 79us/step - loss: 0.4550 - accuracy: 0.8537 - val_loss: 0.6443 - val_accuracy: 0.6182 Epoch 54/300 164/164 [==============================] - 0s 67us/step - loss: 0.4539 - accuracy: 0.8537 - val_loss: 0.6446 - val_accuracy: 0.6182 Epoch 55/300 164/164 [==============================] - 0s 67us/step - loss: 0.4526 - accuracy: 0.8537 - val_loss: 0.6444 - val_accuracy: 0.6182 Epoch 56/300 164/164 [==============================] - 0s 79us/step - loss: 0.4512 - accuracy: 0.8537 - val_loss: 0.6449 - val_accuracy: 0.6182 Epoch 57/300 164/164 [==============================] - 0s 79us/step - loss: 0.4500 - accuracy: 0.8537 - val_loss: 0.6450 - val_accuracy: 0.6182 Epoch 00057: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 58/300 164/164 [==============================] - 0s 73us/step - loss: 0.4492 - accuracy: 0.8659 - val_loss: 0.6445 - val_accuracy: 0.6182 Epoch 59/300 164/164 [==============================] - 0s 73us/step - loss: 0.4485 - accuracy: 0.8659 - val_loss: 0.6441 - val_accuracy: 0.6182 Epoch 60/300 164/164 [==============================] - 0s 73us/step - loss: 0.4481 - accuracy: 0.8659 - val_loss: 0.6449 - val_accuracy: 0.6182 Epoch 61/300 164/164 [==============================] - 0s 79us/step - loss: 0.4471 - accuracy: 0.8659 - val_loss: 0.6459 - val_accuracy: 0.6182 Epoch 62/300 164/164 [==============================] - 0s 91us/step - loss: 0.4467 - accuracy: 0.8659 - val_loss: 0.6465 - val_accuracy: 0.6182 Epoch 63/300 164/164 [==============================] - 0s 85us/step - loss: 0.4459 - accuracy: 0.8659 - val_loss: 0.6467 - val_accuracy: 0.6182 Epoch 64/300 164/164 [==============================] - 0s 61us/step - loss: 0.4453 - accuracy: 0.8659 - val_loss: 0.6471 - val_accuracy: 0.6182 Epoch 65/300 164/164 [==============================] - 0s 67us/step - loss: 0.4448 - accuracy: 0.8659 - val_loss: 0.6470 - val_accuracy: 0.6182 Epoch 66/300 164/164 [==============================] - 0s 73us/step - loss: 0.4440 - accuracy: 0.8659 - val_loss: 0.6466 - val_accuracy: 0.6182 Epoch 67/300 164/164 [==============================] - 0s 73us/step - loss: 0.4439 - accuracy: 0.8659 - val_loss: 0.6464 - val_accuracy: 0.6182 Epoch 00067: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 68/300 164/164 [==============================] - 0s 73us/step - loss: 0.4429 - accuracy: 0.8659 - val_loss: 0.6462 - val_accuracy: 0.6182 Epoch 69/300 164/164 [==============================] - 0s 67us/step - loss: 0.4427 - accuracy: 0.8659 - val_loss: 0.6457 - val_accuracy: 0.6182 Epoch 70/300 164/164 [==============================] - 0s 61us/step - loss: 0.4424 - accuracy: 0.8659 - val_loss: 0.6451 - val_accuracy: 0.6182 Epoch 71/300 164/164 [==============================] - 0s 73us/step - loss: 0.4420 - accuracy: 0.8659 - val_loss: 0.6447 - val_accuracy: 0.6182 Epoch 72/300 164/164 [==============================] - 0s 73us/step - loss: 0.4417 - accuracy: 0.8659 - val_loss: 0.6438 - val_accuracy: 0.6182 Epoch 73/300 164/164 [==============================] - 0s 79us/step - loss: 0.4414 - accuracy: 0.8659 - val_loss: 0.6431 - val_accuracy: 0.6182 Epoch 74/300 164/164 [==============================] - 0s 79us/step - loss: 0.4411 - accuracy: 0.8720 - val_loss: 0.6425 - val_accuracy: 0.6182 Epoch 75/300 164/164 [==============================] - 0s 67us/step - loss: 0.4407 - accuracy: 0.8720 - val_loss: 0.6418 - val_accuracy: 0.6182 Epoch 76/300 164/164 [==============================] - 0s 67us/step - loss: 0.4405 - accuracy: 0.8720 - val_loss: 0.6414 - val_accuracy: 0.6182 Epoch 77/300 164/164 [==============================] - 0s 73us/step - loss: 0.4402 - accuracy: 0.8720 - val_loss: 0.6411 - val_accuracy: 0.6182 Epoch 00077: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 78/300 164/164 [==============================] - 0s 128us/step - loss: 0.4399 - accuracy: 0.8720 - val_loss: 0.6412 - val_accuracy: 0.6182 Epoch 79/300 164/164 [==============================] - 0s 110us/step - loss: 0.4397 - accuracy: 0.8720 - val_loss: 0.6411 - val_accuracy: 0.6182 Epoch 80/300 164/164 [==============================] - 0s 110us/step - loss: 0.4396 - accuracy: 0.8720 - val_loss: 0.6411 - val_accuracy: 0.6182 Epoch 81/300 164/164 [==============================] - 0s 183us/step - loss: 0.4394 - accuracy: 0.8720 - val_loss: 0.6412 - val_accuracy: 0.6182 Epoch 82/300 164/164 [==============================] - 0s 158us/step - loss: 0.4393 - accuracy: 0.8720 - val_loss: 0.6414 - val_accuracy: 0.6182 Epoch 83/300 164/164 [==============================] - 0s 110us/step - loss: 0.4391 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 84/300 164/164 [==============================] - 0s 104us/step - loss: 0.4390 - accuracy: 0.8720 - val_loss: 0.6419 - val_accuracy: 0.6182 Epoch 85/300 164/164 [==============================] - 0s 116us/step - loss: 0.4389 - accuracy: 0.8720 - val_loss: 0.6422 - val_accuracy: 0.6182 Epoch 86/300 164/164 [==============================] - 0s 98us/step - loss: 0.4387 - accuracy: 0.8720 - val_loss: 0.6421 - val_accuracy: 0.6182 Epoch 87/300 164/164 [==============================] - 0s 104us/step - loss: 0.4385 - accuracy: 0.8720 - val_loss: 0.6420 - val_accuracy: 0.6182 Epoch 00087: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 88/300 164/164 [==============================] - 0s 98us/step - loss: 0.4384 - accuracy: 0.8720 - val_loss: 0.6419 - val_accuracy: 0.6182 Epoch 89/300 164/164 [==============================] - 0s 98us/step - loss: 0.4383 - accuracy: 0.8720 - val_loss: 0.6419 - val_accuracy: 0.6182 Epoch 90/300 164/164 [==============================] - 0s 122us/step - loss: 0.4382 - accuracy: 0.8720 - val_loss: 0.6419 - val_accuracy: 0.6182 Epoch 91/300 164/164 [==============================] - 0s 116us/step - loss: 0.4381 - accuracy: 0.8720 - val_loss: 0.6418 - val_accuracy: 0.6182 Epoch 92/300 164/164 [==============================] - 0s 116us/step - loss: 0.4380 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 93/300 164/164 [==============================] - 0s 98us/step - loss: 0.4379 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 94/300 164/164 [==============================] - 0s 104us/step - loss: 0.4379 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 95/300 164/164 [==============================] - 0s 98us/step - loss: 0.4378 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 96/300 164/164 [==============================] - 0s 116us/step - loss: 0.4377 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 97/300 164/164 [==============================] - 0s 98us/step - loss: 0.4376 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 00097: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 98/300 164/164 [==============================] - ETA: 0s - loss: 0.4724 - accuracy: 0.81 - 0s 98us/step - loss: 0.4375 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 99/300 164/164 [==============================] - 0s 104us/step - loss: 0.4375 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 100/300 164/164 [==============================] - 0s 104us/step - loss: 0.4375 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 101/300 164/164 [==============================] - 0s 98us/step - loss: 0.4374 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 102/300 164/164 [==============================] - 0s 104us/step - loss: 0.4374 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 103/300 164/164 [==============================] - 0s 98us/step - loss: 0.4373 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 104/300 164/164 [==============================] - 0s 98us/step - loss: 0.4373 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 105/300 164/164 [==============================] - 0s 98us/step - loss: 0.4373 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 106/300 164/164 [==============================] - 0s 110us/step - loss: 0.4372 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 107/300 164/164 [==============================] - 0s 104us/step - loss: 0.4372 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 00107: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 108/300 164/164 [==============================] - 0s 97us/step - loss: 0.4371 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 109/300 164/164 [==============================] - 0s 104us/step - loss: 0.4371 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 110/300 164/164 [==============================] - 0s 104us/step - loss: 0.4371 - accuracy: 0.8720 - val_loss: 0.6417 - val_accuracy: 0.6182 Epoch 111/300 164/164 [==============================] - 0s 104us/step - loss: 0.4371 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 112/300 164/164 [==============================] - 0s 104us/step - loss: 0.4371 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 113/300 164/164 [==============================] - 0s 110us/step - loss: 0.4370 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 114/300 164/164 [==============================] - 0s 110us/step - loss: 0.4370 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 115/300 164/164 [==============================] - 0s 128us/step - loss: 0.4370 - accuracy: 0.8720 - val_loss: 0.6416 - val_accuracy: 0.6182 Epoch 116/300 164/164 [==============================] - 0s 122us/step - loss: 0.4370 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 117/300 164/164 [==============================] - 0s 122us/step - loss: 0.4370 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00117: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 118/300 164/164 [==============================] - 0s 104us/step - loss: 0.4370 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 119/300 164/164 [==============================] - 0s 104us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 120/300 164/164 [==============================] - 0s 97us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 121/300 164/164 [==============================] - 0s 97us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 122/300 164/164 [==============================] - 0s 91us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 123/300 164/164 [==============================] - 0s 104us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 124/300 164/164 [==============================] - 0s 122us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 125/300 164/164 [==============================] - 0s 98us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 126/300 164/164 [==============================] - 0s 98us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 127/300 164/164 [==============================] - 0s 98us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00127: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 128/300 164/164 [==============================] - 0s 104us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 129/300 164/164 [==============================] - 0s 91us/step - loss: 0.4369 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 130/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 131/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 132/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 133/300 164/164 [==============================] - 0s 225us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 134/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 135/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 136/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 137/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00137: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 138/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 139/300 164/164 [==============================] - 0s 97us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 140/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 141/300 164/164 [==============================] - 0s 97us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 142/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 143/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 144/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 145/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 146/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 147/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00147: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07. Epoch 148/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 149/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 150/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 151/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 152/300 164/164 [==============================] - 0s 134us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 153/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 154/300 164/164 [==============================] - 0s 134us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 155/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 156/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 157/300 164/164 [==============================] - 0s 128us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00157: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07. Epoch 158/300 164/164 [==============================] - 0s 128us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 159/300 164/164 [==============================] - 0s 128us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 160/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 161/300 164/164 [==============================] - 0s 128us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 162/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 163/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 164/300 164/164 [==============================] - 0s 134us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 165/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 166/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 167/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00167: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07. Epoch 168/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 169/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 170/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 171/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 172/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 173/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 174/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 175/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 176/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 177/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00177: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07. Epoch 178/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 179/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 180/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 181/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 182/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 183/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 184/300 164/164 [==============================] - 0s 128us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 185/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 186/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 187/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00187: ReduceLROnPlateau reducing learning rate to 6.103515914901436e-08. Epoch 188/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 189/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 190/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 191/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 192/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 193/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 194/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 195/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 196/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 197/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00197: ReduceLROnPlateau reducing learning rate to 3.051757957450718e-08. Epoch 198/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 199/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 200/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 201/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 202/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 203/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 204/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 205/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 206/300 164/164 [==============================] - 0s 244us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 207/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00207: ReduceLROnPlateau reducing learning rate to 1.525878978725359e-08. Epoch 208/300 164/164 [==============================] - 0s 128us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 209/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 210/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 211/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 212/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 213/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 214/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 215/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 216/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 217/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00217: ReduceLROnPlateau reducing learning rate to 7.629394893626795e-09. Epoch 218/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 219/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 220/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 221/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 222/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 223/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 224/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 225/300 164/164 [==============================] - ETA: 0s - loss: 0.4141 - accuracy: 0.90 - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 226/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 227/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00227: ReduceLROnPlateau reducing learning rate to 3.814697446813398e-09. Epoch 228/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 229/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 230/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 231/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 232/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 233/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 234/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 235/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 236/300 164/164 [==============================] - 0s 262us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 237/300 164/164 [==============================] - 0s 122us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00237: ReduceLROnPlateau reducing learning rate to 1.907348723406699e-09. Epoch 238/300 164/164 [==============================] - 0s 158us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 239/300 164/164 [==============================] - 0s 177us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 240/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 241/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 242/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 243/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 244/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 245/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 246/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 247/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00247: ReduceLROnPlateau reducing learning rate to 9.536743617033494e-10. Epoch 248/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 249/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 250/300 164/164 [==============================] - 0s 128us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 251/300 164/164 [==============================] - 0s 97us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 252/300 164/164 [==============================] - 0s 97us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 253/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 254/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 255/300 164/164 [==============================] - 0s 85us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 256/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 257/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00257: ReduceLROnPlateau reducing learning rate to 4.768371808516747e-10. Epoch 258/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 259/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 260/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 261/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 262/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 263/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 264/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 265/300 164/164 [==============================] - 0s 97us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 266/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 267/300 164/164 [==============================] - 0s 134us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00267: ReduceLROnPlateau reducing learning rate to 2.3841859042583735e-10. Epoch 268/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 269/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 270/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 271/300 164/164 [==============================] - 0s 116us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 272/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 273/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 274/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 275/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 276/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 277/300 164/164 [==============================] - 0s 110us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00277: ReduceLROnPlateau reducing learning rate to 1.1920929521291868e-10. Epoch 278/300 164/164 [==============================] - 0s 97us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 279/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 280/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 281/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 282/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 283/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 284/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 285/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 286/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 287/300 164/164 [==============================] - 0s 97us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00287: ReduceLROnPlateau reducing learning rate to 5.960464760645934e-11. Epoch 288/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 289/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 290/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 291/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 292/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 293/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 294/300 164/164 [==============================] - 0s 98us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 295/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 296/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 297/300 164/164 [==============================] - 0s 104us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 00297: ReduceLROnPlateau reducing learning rate to 2.980232380322967e-11. Epoch 298/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 299/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182 Epoch 300/300 164/164 [==============================] - 0s 91us/step - loss: 0.4368 - accuracy: 0.8720 - val_loss: 0.6415 - val_accuracy: 0.6182
In [110]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 300)
In [111]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
55/55 [==============================] - 0s 73us/step test loss: 0.6415141766721552, test accuracy: 0.6181818246841431
In [112]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6970899470899471
In [113]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.2325581395348838 [[21 7] [14 13]]
KMeans¶
In [179]:
X
Out[179]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.992062 | -0.477172 | -1.079451 | -2.369470 | -1.705431 | -0.098594 | -0.281836 | -1.432001 | -0.898623 | 0.130446 | -0.024683 | -0.312128 | 0.020392 |
| 1 | 0.843575 | -0.507672 | -0.731713 | -0.334904 | 1.442336 | -0.491141 | -0.266416 | -0.511246 | 1.004414 | 0.558777 | 0.127114 | -1.667555 | 0.835458 |
| 2 | 0.816922 | -0.263544 | 0.639646 | -0.865417 | 1.276602 | -0.245238 | 0.106722 | -0.761365 | -0.170481 | -1.443667 | -0.451102 | 1.196430 | -0.037846 |
| 3 | 4.368525 | 0.851784 | -0.671158 | -0.128467 | 2.141169 | -0.472725 | -1.437233 | -1.858760 | 1.581800 | -0.145852 | 0.107228 | 1.458238 | 1.666081 |
| 4 | 0.001312 | 0.535305 | -0.648296 | 0.221414 | 0.549478 | 0.736878 | -0.439538 | -0.138787 | 0.584258 | 0.095671 | 1.901833 | 2.909252 | 1.802578 |
| 5 | -0.236754 | 0.488978 | 0.203743 | 0.088401 | -0.151814 | 0.811707 | -0.092973 | 0.153518 | -0.936863 | 0.354100 | 0.123352 | 1.318569 | 1.097711 |
| 6 | -0.842496 | 0.742173 | 0.068601 | 1.394492 | -0.276167 | 1.301853 | 0.336343 | 1.077540 | -1.118983 | 1.688235 | -0.103661 | 1.224883 | 0.350956 |
| 7 | -0.952702 | 1.078642 | -0.563379 | -0.018149 | -0.073042 | -0.591301 | -1.392389 | 0.209234 | 0.725065 | 0.064350 | 0.034449 | 0.581953 | 2.151966 |
| 8 | 0.046457 | -0.093025 | -0.804385 | 0.542662 | -0.130939 | 0.042792 | 1.198959 | -0.559116 | 0.017192 | -0.249308 | 0.747851 | -0.035599 | 0.995166 |
| 9 | -0.781158 | 0.099463 | 0.196737 | 2.462131 | 0.316140 | -0.369698 | 2.196715 | -0.800443 | 2.137687 | 1.438443 | 0.055279 | -0.284437 | 1.702942 |
| 10 | -0.906167 | 0.568017 | 0.700382 | 2.876646 | -0.809125 | -0.491839 | 1.801564 | -2.406947 | 1.939246 | 1.397556 | 0.709408 | -0.423394 | 1.773713 |
| 11 | 1.172687 | 1.292213 | -0.402038 | 0.087342 | 0.324539 | 0.973336 | -0.548282 | 0.781195 | 0.846038 | 0.464514 | -1.030463 | -0.559243 | 0.168727 |
| 12 | 0.367875 | 1.949889 | 0.516382 | 0.657124 | -0.534306 | 0.575187 | -0.750861 | 0.247200 | -0.232297 | 0.332174 | -0.426787 | 0.318763 | 0.083316 |
| 13 | 1.270520 | 1.194102 | 0.267933 | 0.676186 | 0.394734 | -0.709975 | -0.047626 | 1.113385 | 0.339962 | 0.424937 | -0.528480 | 0.671225 | 0.078062 |
| 14 | -0.095931 | 0.792392 | 0.626113 | 0.189989 | 0.315198 | -0.175744 | 0.011713 | -0.072196 | 0.742338 | 0.974567 | 0.935685 | 0.083454 | 0.970157 |
| 15 | -0.322645 | 0.977766 | 0.685697 | 0.670670 | 0.997903 | 0.619018 | 0.498110 | -0.016728 | 0.445370 | -0.102204 | 0.199517 | -0.315303 | 0.347920 |
| 16 | 0.565974 | 0.440551 | 0.402995 | 1.815814 | 1.906139 | 1.105013 | 1.256180 | 0.907086 | 0.592851 | -0.159427 | 1.013051 | -0.620202 | 1.259932 |
| 17 | -0.863540 | 0.887127 | 1.387720 | -0.082168 | -0.694633 | -0.810037 | 1.251697 | -0.443532 | 0.307506 | 0.253798 | -0.292483 | 0.030812 | 0.176350 |
| 18 | -0.822258 | -0.630193 | -0.672294 | -0.279417 | -0.731983 | -1.510167 | -1.393705 | -0.161872 | 0.722297 | 0.910604 | -0.610303 | 0.380547 | 1.296315 |
| 19 | -0.889164 | 0.641922 | 2.278761 | 0.190213 | -0.341231 | -0.624107 | 1.228820 | -0.549441 | -0.662942 | 0.481866 | -0.541347 | -1.061735 | -0.122227 |
| 20 | 0.795964 | 0.484784 | 0.898919 | 0.027625 | 0.415359 | 0.271286 | 0.366966 | -0.498975 | 0.300352 | 0.216702 | 0.361195 | -0.771976 | 0.085971 |
| 21 | 0.168183 | -0.077353 | 1.019887 | -0.637065 | 0.731534 | 0.877245 | 1.225125 | -0.566997 | -0.452222 | -1.105384 | 0.185636 | -0.782808 | -0.224975 |
| 22 | 0.510023 | -0.099060 | 0.064384 | -0.039933 | 0.786951 | 0.119530 | -0.259052 | -0.881354 | -0.113425 | 1.191274 | 0.335443 | -0.189618 | -0.337688 |
| 23 | 0.216210 | -0.069447 | 0.974822 | -0.626273 | 0.835854 | 0.914236 | 1.226463 | -0.369525 | -0.398299 | -1.146613 | 0.026274 | -0.944475 | -0.192948 |
| 24 | -0.239273 | -0.518568 | -0.127834 | 0.045011 | 0.403223 | 0.368253 | -0.584902 | -0.905436 | -0.405699 | 0.129383 | 0.809611 | -0.174138 | -0.115393 |
| 25 | -1.241907 | 1.355534 | -0.693470 | 0.793789 | 0.606007 | 0.930263 | 0.009323 | -0.712463 | 0.037916 | -0.182143 | 1.212760 | -0.083882 | 0.639662 |
| 26 | -0.847436 | 1.180146 | -0.489592 | 1.189572 | -0.457645 | -0.163979 | -0.010812 | -0.765561 | -0.347488 | -0.216575 | 0.804302 | -0.236378 | 0.481212 |
| 27 | -0.378383 | 1.017722 | -1.812001 | 0.443514 | 0.583209 | 1.709730 | 0.715521 | -0.076610 | 0.416120 | 0.013436 | 0.420025 | -0.925263 | 0.626400 |
| 28 | 0.245370 | 1.187084 | 1.056929 | 2.013063 | -0.505622 | 1.228583 | -1.158143 | 0.622932 | 0.113512 | 0.948397 | 0.008252 | 1.035839 | -0.691702 |
| 29 | -0.623386 | 1.368898 | 1.216933 | 1.961377 | 0.744541 | 1.555516 | -1.205283 | -0.252995 | -0.325624 | 0.538668 | 0.197646 | 0.356450 | -0.219812 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -0.565077 | 0.809784 | 0.557457 | 0.815038 | 0.823053 | -0.931359 | -0.039244 | -0.199068 | 0.083690 | -0.235063 | -0.030800 | -0.564557 | -0.253507 |
| 190 | -0.602848 | 0.638838 | 0.763481 | -0.424641 | -0.810302 | -0.951734 | -0.732024 | -0.504038 | 0.379372 | 0.748895 | -0.593820 | -0.772491 | 0.175752 |
| 191 | -1.094031 | -0.896961 | 0.400325 | -1.635971 | -1.099938 | -1.091799 | -0.593281 | 0.890889 | 0.984647 | 0.584509 | 0.318496 | 0.175062 | -0.783524 |
| 192 | -0.348357 | 0.944340 | 0.239675 | 0.003612 | -1.370450 | -0.996597 | -0.616405 | 0.161481 | -0.258760 | 0.534721 | -0.431338 | 0.376456 | -1.623026 |
| 193 | 2.110671 | -1.005236 | 0.268022 | 0.459390 | -1.985350 | 0.405677 | -0.361571 | -1.272053 | -0.873345 | 2.111218 | -0.246708 | 0.798456 | 1.067252 |
| 194 | 1.222194 | -1.600122 | -1.149302 | 0.230839 | -0.213026 | -1.572114 | 0.486447 | -0.770701 | 0.244895 | 2.689114 | -2.296486 | 0.718338 | -1.220356 |
| 195 | -0.509789 | -0.757711 | 0.189267 | 0.516644 | 0.750906 | -1.485714 | 2.485824 | -1.204754 | -3.373113 | -0.450016 | -1.091178 | -0.474728 | -0.522197 |
| 196 | 0.194175 | -0.618441 | -1.090420 | 0.233017 | -1.492602 | -0.342192 | -1.612833 | 0.714990 | 0.072755 | -0.026932 | 0.464029 | 0.212333 | 1.204262 |
| 197 | 0.297635 | -0.727616 | -1.927078 | -0.145347 | -0.990256 | 0.052935 | -1.791108 | -0.351333 | -0.064903 | 0.201842 | 1.581215 | 1.084453 | -0.168841 |
| 198 | -0.271030 | -0.575137 | -1.005334 | -0.238705 | -0.931830 | -1.319114 | -0.668613 | 0.510822 | 0.209623 | 0.487577 | 0.154874 | 0.133768 | 1.259548 |
| 199 | 0.059096 | -0.370313 | -0.760047 | 0.706270 | -2.488266 | -1.336692 | -0.683584 | 0.436366 | -0.150281 | -0.711308 | -0.851205 | 0.253942 | -0.052516 |
| 200 | 0.147539 | -0.233608 | -0.578016 | 0.870637 | -2.418094 | -1.286070 | -0.692623 | 0.342693 | 0.015890 | -0.795418 | -1.221248 | 0.309493 | -0.526480 |
| 201 | -0.076214 | -1.055629 | 0.159389 | -0.403318 | -0.111273 | -1.325990 | -0.867502 | 0.519381 | 0.192007 | -0.024629 | 0.220420 | 0.551046 | 0.399728 |
| 202 | 1.468986 | 0.518464 | 1.475456 | -1.400891 | 0.408186 | -1.831201 | 1.474742 | 0.566660 | -0.403197 | -1.295176 | -0.443787 | -1.884346 | -1.993491 |
| 203 | -1.739107 | 0.192104 | -0.670709 | -1.236237 | -1.672915 | -0.680127 | 0.027148 | 0.524909 | 1.865754 | -0.634310 | -0.607429 | -1.471191 | -0.632982 |
| 204 | -0.663868 | -0.862566 | -0.329803 | -0.857680 | 0.167824 | -0.013328 | 0.176565 | 0.125832 | 0.609671 | -1.296827 | -0.435986 | -1.341223 | -0.977207 |
| 205 | -0.739818 | -0.668220 | -0.077479 | 0.026286 | 0.027801 | 0.040659 | -0.161646 | -1.046948 | -1.248976 | -0.449243 | 1.046834 | 1.381194 | 1.646325 |
| 206 | 0.475752 | 0.695473 | -0.072097 | 1.081397 | -0.366985 | -2.008080 | 0.515734 | 0.005330 | 1.193800 | -0.841825 | -2.650200 | -3.862624 | -2.115507 |
| 207 | -1.331365 | -1.632552 | -0.876636 | 0.076190 | 1.187799 | 1.138590 | 1.235955 | 1.583447 | 0.890342 | -1.587964 | 0.546109 | 1.565567 | 1.756993 |
| 208 | -0.397476 | 0.090963 | 1.217996 | 0.773741 | 1.107204 | -1.125870 | -0.915396 | -1.130561 | -1.914456 | -0.664474 | -0.226576 | 0.112420 | 0.235011 |
| 209 | -0.465823 | -1.372705 | -0.445436 | 0.316510 | -1.492946 | -1.103783 | 0.353513 | -0.311377 | -1.095388 | -0.615078 | -0.585868 | 0.172807 | -0.860564 |
| 210 | -0.594535 | -1.761364 | -1.069906 | -0.502969 | -1.411276 | -0.906350 | -0.559102 | -1.240920 | -2.254196 | -1.206339 | -0.528047 | 0.924112 | 0.472298 |
| 211 | -1.022693 | 0.373374 | -0.104205 | -0.815628 | -0.574733 | 0.906934 | 0.765114 | -0.015386 | 0.110695 | 1.832325 | 0.712557 | -0.951976 | -0.678869 |
| 212 | -0.967902 | 0.155275 | 0.013938 | -0.549105 | -0.907792 | 0.881907 | 0.609589 | -0.135010 | -0.373473 | 1.152134 | 0.386511 | -0.744687 | -0.447017 |
| 213 | -1.238242 | -0.062983 | -0.133082 | -0.158458 | -0.338086 | -0.411874 | 0.964537 | 0.870379 | 0.530337 | 0.858339 | 0.489332 | -1.190977 | -1.340484 |
| 214 | 0.349761 | -1.391267 | -3.069473 | 0.840195 | 1.044391 | -1.052018 | 1.004856 | 1.478511 | 1.210060 | -1.145325 | 2.653757 | 1.937234 | 0.592139 |
| 215 | 0.782819 | -1.300386 | -0.487318 | 0.850960 | -2.046427 | 1.050631 | 0.289069 | 2.400271 | 2.707288 | -0.278238 | 0.152360 | 1.912210 | -0.208225 |
| 216 | 1.847553 | -1.059174 | -0.808403 | 0.400706 | -0.275009 | 0.409744 | -0.141885 | 0.706348 | 0.476002 | 0.990111 | -0.168504 | 0.856440 | -0.395652 |
| 217 | 2.608478 | 0.174234 | 2.534211 | -0.985597 | -0.436400 | 3.751943 | 1.560179 | -2.367095 | 1.272529 | 2.464209 | -0.954336 | 0.310720 | -1.209456 |
| 218 | -0.069569 | 0.418008 | -0.004324 | 1.330358 | 0.365352 | -0.582788 | -0.527444 | -0.298114 | -0.353021 | -1.118883 | -0.459230 | -0.986241 | -0.041010 |
219 rows × 13 columns
In [180]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[180]:
[2847.0, 2572.5760570812117, 2370.209947155015, 2235.6129406180157, 2112.951551625758, 2041.1809211260454, 1982.3615393500422, 1899.0667595696164, 1851.9267246215204, 1760.4468946465518, 1745.79714786859, 1689.1350809615656, 1657.4940102564742, 1625.370413913055]
In [181]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[181]:
[<matplotlib.lines.Line2D at 0x1b827c5de48>]
K=3
In [182]:
kmeans_mfcc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[182]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [183]:
kmeans_mfcc.labels_
Out[183]:
array([1, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 2,
1, 2, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 0,
0, 0, 1, 1, 2, 0, 2, 2, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 2,
0, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 2, 2, 2,
2, 0, 1, 2, 0, 0, 2, 2, 2, 2, 1, 1, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0,
0, 0, 1, 0, 2, 1, 1, 1, 2, 0, 1, 0, 0, 1, 1, 1, 2, 0, 1, 2, 2, 2,
1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 0, 2, 0, 2, 2, 2, 0, 2,
2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 1, 2, 1, 1, 1, 2, 1, 1,
1, 1, 1, 1, 2, 2, 2, 1, 2, 0, 1, 1, 1, 2, 2, 2, 0, 0, 1, 0, 1])
In [184]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[184]:
array([1, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 2,
1, 2, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 0,
0, 0, 1, 1, 2, 0, 2, 2, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 2,
0, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 2, 2, 2,
2, 0, 1, 2, 0, 0, 2, 2, 2, 2, 1, 1, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0,
0, 0, 1, 0, 2, 1, 1, 1, 2, 0, 1, 0, 0, 1, 1, 1, 2, 0, 1, 2, 2, 2,
1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 0, 2, 0, 2, 2, 2, 0, 2,
2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 1, 2, 1, 1, 1, 2, 1, 1,
1, 1, 1, 1, 2, 2, 2, 1, 2, 0, 1, 1, 1, 2, 2, 2, 0, 0, 1, 0, 1])
In [185]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [186]:
X
Out[186]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.992062 | -0.477172 | -1.079451 | -2.369470 | -1.705431 | -0.098594 | -0.281836 | -1.432001 | -0.898623 | 0.130446 | -0.024683 | -0.312128 | 0.020392 | 1 | 0 |
| 1 | 0.843575 | -0.507672 | -0.731713 | -0.334904 | 1.442336 | -0.491141 | -0.266416 | -0.511246 | 1.004414 | 0.558777 | 0.127114 | -1.667555 | 0.835458 | 2 | 0 |
| 2 | 0.816922 | -0.263544 | 0.639646 | -0.865417 | 1.276602 | -0.245238 | 0.106722 | -0.761365 | -0.170481 | -1.443667 | -0.451102 | 1.196430 | -0.037846 | 1 | 0 |
| 3 | 4.368525 | 0.851784 | -0.671158 | -0.128467 | 2.141169 | -0.472725 | -1.437233 | -1.858760 | 1.581800 | -0.145852 | 0.107228 | 1.458238 | 1.666081 | 1 | 0 |
| 4 | 0.001312 | 0.535305 | -0.648296 | 0.221414 | 0.549478 | 0.736878 | -0.439538 | -0.138787 | 0.584258 | 0.095671 | 1.901833 | 2.909252 | 1.802578 | 0 | 0 |
| 5 | -0.236754 | 0.488978 | 0.203743 | 0.088401 | -0.151814 | 0.811707 | -0.092973 | 0.153518 | -0.936863 | 0.354100 | 0.123352 | 1.318569 | 1.097711 | 0 | 0 |
| 6 | -0.842496 | 0.742173 | 0.068601 | 1.394492 | -0.276167 | 1.301853 | 0.336343 | 1.077540 | -1.118983 | 1.688235 | -0.103661 | 1.224883 | 0.350956 | 0 | 0 |
| 7 | -0.952702 | 1.078642 | -0.563379 | -0.018149 | -0.073042 | -0.591301 | -1.392389 | 0.209234 | 0.725065 | 0.064350 | 0.034449 | 0.581953 | 2.151966 | 1 | 0 |
| 8 | 0.046457 | -0.093025 | -0.804385 | 0.542662 | -0.130939 | 0.042792 | 1.198959 | -0.559116 | 0.017192 | -0.249308 | 0.747851 | -0.035599 | 0.995166 | 0 | 0 |
| 9 | -0.781158 | 0.099463 | 0.196737 | 2.462131 | 0.316140 | -0.369698 | 2.196715 | -0.800443 | 2.137687 | 1.438443 | 0.055279 | -0.284437 | 1.702942 | 0 | 0 |
| 10 | -0.906167 | 0.568017 | 0.700382 | 2.876646 | -0.809125 | -0.491839 | 1.801564 | -2.406947 | 1.939246 | 1.397556 | 0.709408 | -0.423394 | 1.773713 | 0 | 0 |
| 11 | 1.172687 | 1.292213 | -0.402038 | 0.087342 | 0.324539 | 0.973336 | -0.548282 | 0.781195 | 0.846038 | 0.464514 | -1.030463 | -0.559243 | 0.168727 | 0 | 0 |
| 12 | 0.367875 | 1.949889 | 0.516382 | 0.657124 | -0.534306 | 0.575187 | -0.750861 | 0.247200 | -0.232297 | 0.332174 | -0.426787 | 0.318763 | 0.083316 | 0 | 0 |
| 13 | 1.270520 | 1.194102 | 0.267933 | 0.676186 | 0.394734 | -0.709975 | -0.047626 | 1.113385 | 0.339962 | 0.424937 | -0.528480 | 0.671225 | 0.078062 | 0 | 0 |
| 14 | -0.095931 | 0.792392 | 0.626113 | 0.189989 | 0.315198 | -0.175744 | 0.011713 | -0.072196 | 0.742338 | 0.974567 | 0.935685 | 0.083454 | 0.970157 | 0 | 0 |
| 15 | -0.322645 | 0.977766 | 0.685697 | 0.670670 | 0.997903 | 0.619018 | 0.498110 | -0.016728 | 0.445370 | -0.102204 | 0.199517 | -0.315303 | 0.347920 | 0 | 0 |
| 16 | 0.565974 | 0.440551 | 0.402995 | 1.815814 | 1.906139 | 1.105013 | 1.256180 | 0.907086 | 0.592851 | -0.159427 | 1.013051 | -0.620202 | 1.259932 | 0 | 0 |
| 17 | -0.863540 | 0.887127 | 1.387720 | -0.082168 | -0.694633 | -0.810037 | 1.251697 | -0.443532 | 0.307506 | 0.253798 | -0.292483 | 0.030812 | 0.176350 | 0 | 0 |
| 18 | -0.822258 | -0.630193 | -0.672294 | -0.279417 | -0.731983 | -1.510167 | -1.393705 | -0.161872 | 0.722297 | 0.910604 | -0.610303 | 0.380547 | 1.296315 | 1 | 0 |
| 19 | -0.889164 | 0.641922 | 2.278761 | 0.190213 | -0.341231 | -0.624107 | 1.228820 | -0.549441 | -0.662942 | 0.481866 | -0.541347 | -1.061735 | -0.122227 | 2 | 0 |
| 20 | 0.795964 | 0.484784 | 0.898919 | 0.027625 | 0.415359 | 0.271286 | 0.366966 | -0.498975 | 0.300352 | 0.216702 | 0.361195 | -0.771976 | 0.085971 | 0 | 0 |
| 21 | 0.168183 | -0.077353 | 1.019887 | -0.637065 | 0.731534 | 0.877245 | 1.225125 | -0.566997 | -0.452222 | -1.105384 | 0.185636 | -0.782808 | -0.224975 | 2 | 0 |
| 22 | 0.510023 | -0.099060 | 0.064384 | -0.039933 | 0.786951 | 0.119530 | -0.259052 | -0.881354 | -0.113425 | 1.191274 | 0.335443 | -0.189618 | -0.337688 | 1 | 0 |
| 23 | 0.216210 | -0.069447 | 0.974822 | -0.626273 | 0.835854 | 0.914236 | 1.226463 | -0.369525 | -0.398299 | -1.146613 | 0.026274 | -0.944475 | -0.192948 | 2 | 0 |
| 24 | -0.239273 | -0.518568 | -0.127834 | 0.045011 | 0.403223 | 0.368253 | -0.584902 | -0.905436 | -0.405699 | 0.129383 | 0.809611 | -0.174138 | -0.115393 | 1 | 0 |
| 25 | -1.241907 | 1.355534 | -0.693470 | 0.793789 | 0.606007 | 0.930263 | 0.009323 | -0.712463 | 0.037916 | -0.182143 | 1.212760 | -0.083882 | 0.639662 | 0 | 0 |
| 26 | -0.847436 | 1.180146 | -0.489592 | 1.189572 | -0.457645 | -0.163979 | -0.010812 | -0.765561 | -0.347488 | -0.216575 | 0.804302 | -0.236378 | 0.481212 | 0 | 0 |
| 27 | -0.378383 | 1.017722 | -1.812001 | 0.443514 | 0.583209 | 1.709730 | 0.715521 | -0.076610 | 0.416120 | 0.013436 | 0.420025 | -0.925263 | 0.626400 | 0 | 0 |
| 28 | 0.245370 | 1.187084 | 1.056929 | 2.013063 | -0.505622 | 1.228583 | -1.158143 | 0.622932 | 0.113512 | 0.948397 | 0.008252 | 1.035839 | -0.691702 | 0 | 0 |
| 29 | -0.623386 | 1.368898 | 1.216933 | 1.961377 | 0.744541 | 1.555516 | -1.205283 | -0.252995 | -0.325624 | 0.538668 | 0.197646 | 0.356450 | -0.219812 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -0.565077 | 0.809784 | 0.557457 | 0.815038 | 0.823053 | -0.931359 | -0.039244 | -0.199068 | 0.083690 | -0.235063 | -0.030800 | -0.564557 | -0.253507 | 0 | 1 |
| 190 | -0.602848 | 0.638838 | 0.763481 | -0.424641 | -0.810302 | -0.951734 | -0.732024 | -0.504038 | 0.379372 | 0.748895 | -0.593820 | -0.772491 | 0.175752 | 1 | 1 |
| 191 | -1.094031 | -0.896961 | 0.400325 | -1.635971 | -1.099938 | -1.091799 | -0.593281 | 0.890889 | 0.984647 | 0.584509 | 0.318496 | 0.175062 | -0.783524 | 2 | 1 |
| 192 | -0.348357 | 0.944340 | 0.239675 | 0.003612 | -1.370450 | -0.996597 | -0.616405 | 0.161481 | -0.258760 | 0.534721 | -0.431338 | 0.376456 | -1.623026 | 1 | 1 |
| 193 | 2.110671 | -1.005236 | 0.268022 | 0.459390 | -1.985350 | 0.405677 | -0.361571 | -1.272053 | -0.873345 | 2.111218 | -0.246708 | 0.798456 | 1.067252 | 1 | 1 |
| 194 | 1.222194 | -1.600122 | -1.149302 | 0.230839 | -0.213026 | -1.572114 | 0.486447 | -0.770701 | 0.244895 | 2.689114 | -2.296486 | 0.718338 | -1.220356 | 1 | 1 |
| 195 | -0.509789 | -0.757711 | 0.189267 | 0.516644 | 0.750906 | -1.485714 | 2.485824 | -1.204754 | -3.373113 | -0.450016 | -1.091178 | -0.474728 | -0.522197 | 2 | 1 |
| 196 | 0.194175 | -0.618441 | -1.090420 | 0.233017 | -1.492602 | -0.342192 | -1.612833 | 0.714990 | 0.072755 | -0.026932 | 0.464029 | 0.212333 | 1.204262 | 1 | 1 |
| 197 | 0.297635 | -0.727616 | -1.927078 | -0.145347 | -0.990256 | 0.052935 | -1.791108 | -0.351333 | -0.064903 | 0.201842 | 1.581215 | 1.084453 | -0.168841 | 1 | 1 |
| 198 | -0.271030 | -0.575137 | -1.005334 | -0.238705 | -0.931830 | -1.319114 | -0.668613 | 0.510822 | 0.209623 | 0.487577 | 0.154874 | 0.133768 | 1.259548 | 1 | 1 |
| 199 | 0.059096 | -0.370313 | -0.760047 | 0.706270 | -2.488266 | -1.336692 | -0.683584 | 0.436366 | -0.150281 | -0.711308 | -0.851205 | 0.253942 | -0.052516 | 1 | 1 |
| 200 | 0.147539 | -0.233608 | -0.578016 | 0.870637 | -2.418094 | -1.286070 | -0.692623 | 0.342693 | 0.015890 | -0.795418 | -1.221248 | 0.309493 | -0.526480 | 1 | 1 |
| 201 | -0.076214 | -1.055629 | 0.159389 | -0.403318 | -0.111273 | -1.325990 | -0.867502 | 0.519381 | 0.192007 | -0.024629 | 0.220420 | 0.551046 | 0.399728 | 1 | 1 |
| 202 | 1.468986 | 0.518464 | 1.475456 | -1.400891 | 0.408186 | -1.831201 | 1.474742 | 0.566660 | -0.403197 | -1.295176 | -0.443787 | -1.884346 | -1.993491 | 2 | 1 |
| 203 | -1.739107 | 0.192104 | -0.670709 | -1.236237 | -1.672915 | -0.680127 | 0.027148 | 0.524909 | 1.865754 | -0.634310 | -0.607429 | -1.471191 | -0.632982 | 2 | 1 |
| 204 | -0.663868 | -0.862566 | -0.329803 | -0.857680 | 0.167824 | -0.013328 | 0.176565 | 0.125832 | 0.609671 | -1.296827 | -0.435986 | -1.341223 | -0.977207 | 2 | 1 |
| 205 | -0.739818 | -0.668220 | -0.077479 | 0.026286 | 0.027801 | 0.040659 | -0.161646 | -1.046948 | -1.248976 | -0.449243 | 1.046834 | 1.381194 | 1.646325 | 1 | 1 |
| 206 | 0.475752 | 0.695473 | -0.072097 | 1.081397 | -0.366985 | -2.008080 | 0.515734 | 0.005330 | 1.193800 | -0.841825 | -2.650200 | -3.862624 | -2.115507 | 2 | 1 |
| 207 | -1.331365 | -1.632552 | -0.876636 | 0.076190 | 1.187799 | 1.138590 | 1.235955 | 1.583447 | 0.890342 | -1.587964 | 0.546109 | 1.565567 | 1.756993 | 0 | 1 |
| 208 | -0.397476 | 0.090963 | 1.217996 | 0.773741 | 1.107204 | -1.125870 | -0.915396 | -1.130561 | -1.914456 | -0.664474 | -0.226576 | 0.112420 | 0.235011 | 1 | 1 |
| 209 | -0.465823 | -1.372705 | -0.445436 | 0.316510 | -1.492946 | -1.103783 | 0.353513 | -0.311377 | -1.095388 | -0.615078 | -0.585868 | 0.172807 | -0.860564 | 1 | 1 |
| 210 | -0.594535 | -1.761364 | -1.069906 | -0.502969 | -1.411276 | -0.906350 | -0.559102 | -1.240920 | -2.254196 | -1.206339 | -0.528047 | 0.924112 | 0.472298 | 1 | 1 |
| 211 | -1.022693 | 0.373374 | -0.104205 | -0.815628 | -0.574733 | 0.906934 | 0.765114 | -0.015386 | 0.110695 | 1.832325 | 0.712557 | -0.951976 | -0.678869 | 2 | 1 |
| 212 | -0.967902 | 0.155275 | 0.013938 | -0.549105 | -0.907792 | 0.881907 | 0.609589 | -0.135010 | -0.373473 | 1.152134 | 0.386511 | -0.744687 | -0.447017 | 2 | 1 |
| 213 | -1.238242 | -0.062983 | -0.133082 | -0.158458 | -0.338086 | -0.411874 | 0.964537 | 0.870379 | 0.530337 | 0.858339 | 0.489332 | -1.190977 | -1.340484 | 2 | 1 |
| 214 | 0.349761 | -1.391267 | -3.069473 | 0.840195 | 1.044391 | -1.052018 | 1.004856 | 1.478511 | 1.210060 | -1.145325 | 2.653757 | 1.937234 | 0.592139 | 0 | 1 |
| 215 | 0.782819 | -1.300386 | -0.487318 | 0.850960 | -2.046427 | 1.050631 | 0.289069 | 2.400271 | 2.707288 | -0.278238 | 0.152360 | 1.912210 | -0.208225 | 0 | 1 |
| 216 | 1.847553 | -1.059174 | -0.808403 | 0.400706 | -0.275009 | 0.409744 | -0.141885 | 0.706348 | 0.476002 | 0.990111 | -0.168504 | 0.856440 | -0.395652 | 1 | 1 |
| 217 | 2.608478 | 0.174234 | 2.534211 | -0.985597 | -0.436400 | 3.751943 | 1.560179 | -2.367095 | 1.272529 | 2.464209 | -0.954336 | 0.310720 | -1.209456 | 0 | 1 |
| 218 | -0.069569 | 0.418008 | -0.004324 | 1.330358 | 0.365352 | -0.582788 | -0.527444 | -0.298114 | -0.353021 | -1.118883 | -0.459230 | -0.986241 | -0.041010 | 1 | 1 |
219 rows × 15 columns
In [187]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[187]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b827cab550>
In [188]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[5]))
Urban Place¶
ANN¶
In [114]:
X = df_n_ps_std_mfcc[5]
In [115]:
y = df_n_ps[5]['chosen']
In [116]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [117]:
X_train.shape
Out[117]:
(162, 13)
In [118]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [119]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [120]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [196]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'logistic', 'hidden_layer_sizes': (30, 30), 'learning_rate_init': 0.009, 'max_iter': 1000}, que permiten obtener un Accuracy de 72.84% y un Kappa del 19.82
Tiempo total: 33.32 minutos
In [121]:
grid.best_params_= {'activation': 'sigmoid', 'hidden_layer_sizes': (30, 30), 'learning_rate_init': 0.009, 'max_iter': 1000}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [122]:
input_tensor = Input(shape = (n0,))
In [123]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [124]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [125]:
model.summary()
Model: "model_6" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_7 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_17 (Dense) (None, 30) 420 _________________________________________________________________ dense_18 (Dense) (None, 30) 930 _________________________________________________________________ dense_19 (Dense) (None, 1) 31 ================================================================= Total params: 1,381 Trainable params: 1,381 Non-trainable params: 0 _________________________________________________________________
In [126]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 162 samples, validate on 54 samples Epoch 1/1000 162/162 [==============================] - 0s 1ms/step - loss: 0.6693 - accuracy: 0.6296 - val_loss: 0.5670 - val_accuracy: 0.7778 Epoch 2/1000 162/162 [==============================] - 0s 86us/step - loss: 0.6681 - accuracy: 0.6296 - val_loss: 0.5417 - val_accuracy: 0.7778 Epoch 3/1000 162/162 [==============================] - 0s 80us/step - loss: 0.6660 - accuracy: 0.6296 - val_loss: 0.6625 - val_accuracy: 0.7407 Epoch 4/1000 162/162 [==============================] - 0s 117us/step - loss: 0.6927 - accuracy: 0.4938 - val_loss: 0.6973 - val_accuracy: 0.3704 Epoch 5/1000 162/162 [==============================] - 0s 86us/step - loss: 0.6667 - accuracy: 0.6296 - val_loss: 0.5859 - val_accuracy: 0.7778 Epoch 6/1000 162/162 [==============================] - 0s 74us/step - loss: 0.6472 - accuracy: 0.6296 - val_loss: 0.5762 - val_accuracy: 0.7778 Epoch 7/1000 162/162 [==============================] - 0s 86us/step - loss: 0.6449 - accuracy: 0.6296 - val_loss: 0.5856 - val_accuracy: 0.7778 Epoch 8/1000 162/162 [==============================] - 0s 74us/step - loss: 0.6488 - accuracy: 0.6358 - val_loss: 0.6235 - val_accuracy: 0.7963 Epoch 9/1000 162/162 [==============================] - 0s 80us/step - loss: 0.6424 - accuracy: 0.6543 - val_loss: 0.5655 - val_accuracy: 0.7778 Epoch 10/1000 162/162 [==============================] - 0s 68us/step - loss: 0.6346 - accuracy: 0.6296 - val_loss: 0.5359 - val_accuracy: 0.7778 Epoch 11/1000 162/162 [==============================] - 0s 74us/step - loss: 0.6399 - accuracy: 0.6296 - val_loss: 0.5650 - val_accuracy: 0.8148 Epoch 12/1000 162/162 [==============================] - 0s 80us/step - loss: 0.6252 - accuracy: 0.6543 - val_loss: 0.5501 - val_accuracy: 0.7778 Epoch 13/1000 162/162 [==============================] - 0s 86us/step - loss: 0.6219 - accuracy: 0.6481 - val_loss: 0.5589 - val_accuracy: 0.8148 Epoch 14/1000 162/162 [==============================] - 0s 80us/step - loss: 0.6241 - accuracy: 0.6420 - val_loss: 0.5368 - val_accuracy: 0.7778 Epoch 15/1000 162/162 [==============================] - 0s 86us/step - loss: 0.6218 - accuracy: 0.6420 - val_loss: 0.5363 - val_accuracy: 0.7963 Epoch 16/1000 162/162 [==============================] - 0s 74us/step - loss: 0.6086 - accuracy: 0.6481 - val_loss: 0.5852 - val_accuracy: 0.7407 Epoch 17/1000 162/162 [==============================] - 0s 68us/step - loss: 0.6120 - accuracy: 0.6173 - val_loss: 0.5719 - val_accuracy: 0.7407 Epoch 18/1000 162/162 [==============================] - 0s 68us/step - loss: 0.6059 - accuracy: 0.6111 - val_loss: 0.5646 - val_accuracy: 0.7407 Epoch 19/1000 162/162 [==============================] - 0s 80us/step - loss: 0.6005 - accuracy: 0.6358 - val_loss: 0.5501 - val_accuracy: 0.7407 Epoch 20/1000 162/162 [==============================] - 0s 74us/step - loss: 0.5992 - accuracy: 0.6296 - val_loss: 0.5583 - val_accuracy: 0.7407 Epoch 21/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5981 - accuracy: 0.6481 - val_loss: 0.6057 - val_accuracy: 0.6296 Epoch 00021: ReduceLROnPlateau reducing learning rate to 0.0044999998062849045. Epoch 22/1000 162/162 [==============================] - 0s 86us/step - loss: 0.6104 - accuracy: 0.6728 - val_loss: 0.6085 - val_accuracy: 0.6111 Epoch 23/1000 162/162 [==============================] - 0s 86us/step - loss: 0.6104 - accuracy: 0.6728 - val_loss: 0.5901 - val_accuracy: 0.6852 Epoch 24/1000 162/162 [==============================] - 0s 80us/step - loss: 0.6000 - accuracy: 0.6605 - val_loss: 0.5539 - val_accuracy: 0.7407 Epoch 25/1000 162/162 [==============================] - 0s 74us/step - loss: 0.5960 - accuracy: 0.6235 - val_loss: 0.5391 - val_accuracy: 0.7407 Epoch 26/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5942 - accuracy: 0.6605 - val_loss: 0.5358 - val_accuracy: 0.7407 Epoch 27/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5937 - accuracy: 0.6667 - val_loss: 0.5303 - val_accuracy: 0.7407 Epoch 28/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5939 - accuracy: 0.6605 - val_loss: 0.5275 - val_accuracy: 0.7593 Epoch 29/1000 162/162 [==============================] - 0s 74us/step - loss: 0.5968 - accuracy: 0.6543 - val_loss: 0.5256 - val_accuracy: 0.7593 Epoch 30/1000 162/162 [==============================] - 0s 74us/step - loss: 0.5941 - accuracy: 0.6605 - val_loss: 0.5381 - val_accuracy: 0.7407 Epoch 31/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5906 - accuracy: 0.6481 - val_loss: 0.5447 - val_accuracy: 0.7407 Epoch 00031: ReduceLROnPlateau reducing learning rate to 0.0022499999031424522. Epoch 32/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5887 - accuracy: 0.6296 - val_loss: 0.5487 - val_accuracy: 0.7222 Epoch 33/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5872 - accuracy: 0.6420 - val_loss: 0.5544 - val_accuracy: 0.7222 Epoch 34/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5863 - accuracy: 0.6543 - val_loss: 0.5616 - val_accuracy: 0.7037 Epoch 35/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5880 - accuracy: 0.6728 - val_loss: 0.5678 - val_accuracy: 0.7037 Epoch 36/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5871 - accuracy: 0.6605 - val_loss: 0.5671 - val_accuracy: 0.7037 Epoch 37/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5873 - accuracy: 0.6667 - val_loss: 0.5772 - val_accuracy: 0.6667 Epoch 38/1000 162/162 [==============================] - 0s 62us/step - loss: 0.5911 - accuracy: 0.6667 - val_loss: 0.5921 - val_accuracy: 0.6481 Epoch 39/1000 162/162 [==============================] - 0s 68us/step - loss: 0.5906 - accuracy: 0.6728 - val_loss: 0.5819 - val_accuracy: 0.6481 Epoch 40/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5884 - accuracy: 0.6605 - val_loss: 0.5711 - val_accuracy: 0.6852 Epoch 41/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5847 - accuracy: 0.6667 - val_loss: 0.5655 - val_accuracy: 0.7037 Epoch 00041: ReduceLROnPlateau reducing learning rate to 0.0011249999515712261. Epoch 42/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5840 - accuracy: 0.6790 - val_loss: 0.5584 - val_accuracy: 0.7037 Epoch 43/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5841 - accuracy: 0.6543 - val_loss: 0.5531 - val_accuracy: 0.7222 Epoch 44/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5840 - accuracy: 0.6543 - val_loss: 0.5525 - val_accuracy: 0.7222 Epoch 45/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5830 - accuracy: 0.6543 - val_loss: 0.5550 - val_accuracy: 0.7222 Epoch 46/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5829 - accuracy: 0.6481 - val_loss: 0.5579 - val_accuracy: 0.7037 Epoch 47/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5836 - accuracy: 0.6667 - val_loss: 0.5630 - val_accuracy: 0.7037 Epoch 48/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5835 - accuracy: 0.6728 - val_loss: 0.5657 - val_accuracy: 0.7037 Epoch 49/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5822 - accuracy: 0.6728 - val_loss: 0.5614 - val_accuracy: 0.7037 Epoch 50/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5824 - accuracy: 0.6667 - val_loss: 0.5586 - val_accuracy: 0.7037 Epoch 51/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5821 - accuracy: 0.6605 - val_loss: 0.5580 - val_accuracy: 0.7037 Epoch 00051: ReduceLROnPlateau reducing learning rate to 0.0005624999757856131. Epoch 52/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5818 - accuracy: 0.6605 - val_loss: 0.5587 - val_accuracy: 0.7037 Epoch 53/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5816 - accuracy: 0.6605 - val_loss: 0.5568 - val_accuracy: 0.7037 Epoch 54/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5816 - accuracy: 0.6605 - val_loss: 0.5565 - val_accuracy: 0.7037 Epoch 55/1000 162/162 [==============================] - 0s 68us/step - loss: 0.5815 - accuracy: 0.6605 - val_loss: 0.5575 - val_accuracy: 0.7037 Epoch 56/1000 162/162 [==============================] - 0s 68us/step - loss: 0.5813 - accuracy: 0.6605 - val_loss: 0.5588 - val_accuracy: 0.7037 Epoch 57/1000 162/162 [==============================] - 0s 74us/step - loss: 0.5809 - accuracy: 0.6605 - val_loss: 0.5629 - val_accuracy: 0.7037 Epoch 58/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5814 - accuracy: 0.6728 - val_loss: 0.5669 - val_accuracy: 0.7037 Epoch 59/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5819 - accuracy: 0.6728 - val_loss: 0.5681 - val_accuracy: 0.6852 Epoch 60/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5819 - accuracy: 0.6728 - val_loss: 0.5651 - val_accuracy: 0.7037 Epoch 61/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5817 - accuracy: 0.6728 - val_loss: 0.5666 - val_accuracy: 0.6852 Epoch 00061: ReduceLROnPlateau reducing learning rate to 0.00028124998789280653. Epoch 62/1000 162/162 [==============================] - 0s 74us/step - loss: 0.5816 - accuracy: 0.6728 - val_loss: 0.5674 - val_accuracy: 0.6852 Epoch 63/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5818 - accuracy: 0.6790 - val_loss: 0.5691 - val_accuracy: 0.6852 Epoch 64/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5820 - accuracy: 0.6790 - val_loss: 0.5677 - val_accuracy: 0.6852 Epoch 65/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5816 - accuracy: 0.6790 - val_loss: 0.5678 - val_accuracy: 0.6852 Epoch 66/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5816 - accuracy: 0.6790 - val_loss: 0.5681 - val_accuracy: 0.6852 Epoch 67/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5816 - accuracy: 0.6790 - val_loss: 0.5675 - val_accuracy: 0.6852 Epoch 68/1000 162/162 [==============================] - 0s 74us/step - loss: 0.5815 - accuracy: 0.6667 - val_loss: 0.5660 - val_accuracy: 0.6852 Epoch 69/1000 162/162 [==============================] - 0s 68us/step - loss: 0.5812 - accuracy: 0.6728 - val_loss: 0.5650 - val_accuracy: 0.7037 Epoch 70/1000 162/162 [==============================] - 0s 62us/step - loss: 0.5811 - accuracy: 0.6728 - val_loss: 0.5631 - val_accuracy: 0.7037 Epoch 71/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5806 - accuracy: 0.6667 - val_loss: 0.5606 - val_accuracy: 0.7037 Epoch 00071: ReduceLROnPlateau reducing learning rate to 0.00014062499394640326. Epoch 72/1000 162/162 [==============================] - 0s 74us/step - loss: 0.5806 - accuracy: 0.6605 - val_loss: 0.5601 - val_accuracy: 0.7037 Epoch 73/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5806 - accuracy: 0.6605 - val_loss: 0.5599 - val_accuracy: 0.7037 Epoch 74/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5805 - accuracy: 0.6605 - val_loss: 0.5594 - val_accuracy: 0.7037 Epoch 75/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5805 - accuracy: 0.6605 - val_loss: 0.5597 - val_accuracy: 0.7037 Epoch 76/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5805 - accuracy: 0.6605 - val_loss: 0.5604 - val_accuracy: 0.7037 Epoch 77/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5805 - accuracy: 0.6605 - val_loss: 0.5607 - val_accuracy: 0.7037 Epoch 78/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5804 - accuracy: 0.6605 - val_loss: 0.5605 - val_accuracy: 0.7037 Epoch 79/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5804 - accuracy: 0.6605 - val_loss: 0.5601 - val_accuracy: 0.7037 Epoch 80/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5804 - accuracy: 0.6605 - val_loss: 0.5599 - val_accuracy: 0.7037 Epoch 81/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5804 - accuracy: 0.6605 - val_loss: 0.5593 - val_accuracy: 0.7037 Epoch 00081: ReduceLROnPlateau reducing learning rate to 7.031249697320163e-05. Epoch 82/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5803 - accuracy: 0.6605 - val_loss: 0.5593 - val_accuracy: 0.7037 Epoch 83/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5803 - accuracy: 0.6605 - val_loss: 0.5593 - val_accuracy: 0.7037 Epoch 84/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5803 - accuracy: 0.6605 - val_loss: 0.5588 - val_accuracy: 0.7037 Epoch 85/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5584 - val_accuracy: 0.7037 Epoch 86/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5584 - val_accuracy: 0.7037 Epoch 87/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5582 - val_accuracy: 0.7037 Epoch 88/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5582 - val_accuracy: 0.7037 Epoch 89/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5579 - val_accuracy: 0.7037 Epoch 90/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 91/1000 162/162 [==============================] - 0s 290us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5575 - val_accuracy: 0.7037 Epoch 00091: ReduceLROnPlateau reducing learning rate to 3.5156248486600816e-05. Epoch 92/1000 162/162 [==============================] - 0s 271us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5576 - val_accuracy: 0.7037 Epoch 93/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5576 - val_accuracy: 0.7037 Epoch 94/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5576 - val_accuracy: 0.7037 Epoch 95/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5802 - accuracy: 0.6605 - val_loss: 0.5574 - val_accuracy: 0.7037 Epoch 96/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5574 - val_accuracy: 0.7037 Epoch 97/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5575 - val_accuracy: 0.7037 Epoch 98/1000 162/162 [==============================] - 0s 185us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5574 - val_accuracy: 0.7037 Epoch 99/1000 162/162 [==============================] - 0s 154us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5573 - val_accuracy: 0.7037 Epoch 100/1000 162/162 [==============================] - 0s 197us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5574 - val_accuracy: 0.7037 Epoch 101/1000 162/162 [==============================] - 0s 173us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5575 - val_accuracy: 0.7037 Epoch 00101: ReduceLROnPlateau reducing learning rate to 1.7578124243300408e-05. Epoch 102/1000 162/162 [==============================] - 0s 179us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5575 - val_accuracy: 0.7037 Epoch 103/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5576 - val_accuracy: 0.7037 Epoch 104/1000 162/162 [==============================] - 0s 154us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 105/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 106/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 107/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5576 - val_accuracy: 0.7037 Epoch 108/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5575 - val_accuracy: 0.7037 Epoch 109/1000 162/162 [==============================] - 0s 154us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5576 - val_accuracy: 0.7037 Epoch 110/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5575 - val_accuracy: 0.7037 Epoch 111/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5576 - val_accuracy: 0.7037 Epoch 00111: ReduceLROnPlateau reducing learning rate to 8.789062121650204e-06. Epoch 112/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5576 - val_accuracy: 0.7037 Epoch 113/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 114/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 115/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 116/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 117/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 118/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 119/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 120/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 121/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 00121: ReduceLROnPlateau reducing learning rate to 4.394531060825102e-06. Epoch 122/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 123/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 124/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 125/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 126/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 127/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 128/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 129/1000 162/162 [==============================] - 0s 154us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 130/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5578 - val_accuracy: 0.7037 Epoch 131/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00131: ReduceLROnPlateau reducing learning rate to 2.197265530412551e-06. Epoch 132/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 133/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 134/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 135/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 136/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 137/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 138/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 139/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 140/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 141/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00141: ReduceLROnPlateau reducing learning rate to 1.0986327652062755e-06. Epoch 142/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 143/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 144/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 145/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 146/1000 162/162 [==============================] - 0s 154us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 147/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 148/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 149/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 150/1000 162/162 [==============================] - 0s 154us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 151/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00151: ReduceLROnPlateau reducing learning rate to 5.493163826031378e-07. Epoch 152/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 153/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 154/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 155/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 156/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 157/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 158/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 159/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 160/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 161/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00161: ReduceLROnPlateau reducing learning rate to 2.746581913015689e-07. Epoch 162/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 163/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 164/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 165/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 166/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5801 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 167/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 168/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 169/1000 162/162 [==============================] - 0s 167us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 170/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 171/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00171: ReduceLROnPlateau reducing learning rate to 1.3732909565078444e-07. Epoch 172/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 173/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 174/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 175/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 176/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 177/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 178/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 179/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 180/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 181/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00181: ReduceLROnPlateau reducing learning rate to 6.866454782539222e-08. Epoch 182/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 183/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 184/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 185/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 186/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 187/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 188/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 189/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 190/1000 162/162 [==============================] - 0s 154us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 191/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00191: ReduceLROnPlateau reducing learning rate to 3.433227391269611e-08. Epoch 192/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 193/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 194/1000 162/162 [==============================] - 0s 197us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 195/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 196/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 197/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 198/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 199/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 200/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 201/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00201: ReduceLROnPlateau reducing learning rate to 1.7166136956348055e-08. Epoch 202/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 203/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 204/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 205/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 206/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 207/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 208/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 209/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 210/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 211/1000 162/162 [==============================] - 0s 234us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00211: ReduceLROnPlateau reducing learning rate to 8.583068478174027e-09. Epoch 212/1000 162/162 [==============================] - 0s 173us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 213/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 214/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 215/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 216/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 217/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 218/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 219/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 220/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 221/1000 162/162 [==============================] - 0s 2ms/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00221: ReduceLROnPlateau reducing learning rate to 4.291534239087014e-09. Epoch 222/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 223/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 224/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 225/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 226/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 227/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 228/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 229/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 230/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 231/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00231: ReduceLROnPlateau reducing learning rate to 2.145767119543507e-09. Epoch 232/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 233/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 234/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 235/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 236/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 237/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 238/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 239/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 240/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 241/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00241: ReduceLROnPlateau reducing learning rate to 1.0728835597717534e-09. Epoch 242/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 243/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 244/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 245/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 246/1000 162/162 [==============================] - 0s 154us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 247/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 248/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 249/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 250/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 251/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00251: ReduceLROnPlateau reducing learning rate to 5.364417798858767e-10. Epoch 252/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 253/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 254/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 255/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 256/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 257/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 258/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 259/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 260/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 261/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00261: ReduceLROnPlateau reducing learning rate to 2.6822088994293836e-10. Epoch 262/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 263/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 264/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 265/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 266/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 267/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 268/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 269/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 270/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 271/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00271: ReduceLROnPlateau reducing learning rate to 1.3411044497146918e-10. Epoch 272/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 273/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 274/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 275/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 276/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 277/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 278/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 279/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 280/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 281/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00281: ReduceLROnPlateau reducing learning rate to 6.705522248573459e-11. Epoch 282/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 283/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 284/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 285/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 286/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 287/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 288/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 289/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 290/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 291/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00291: ReduceLROnPlateau reducing learning rate to 3.3527611242867295e-11. Epoch 292/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 293/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 294/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 295/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 296/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 297/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 298/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 299/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 300/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 301/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00301: ReduceLROnPlateau reducing learning rate to 1.6763805621433647e-11. Epoch 302/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 303/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 304/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 305/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 306/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 307/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 308/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 309/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 310/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 311/1000 162/162 [==============================] - 0s 86us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00311: ReduceLROnPlateau reducing learning rate to 8.381902810716824e-12. Epoch 312/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 313/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 314/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 315/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 316/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 317/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 318/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 319/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 320/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 321/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00321: ReduceLROnPlateau reducing learning rate to 4.190951405358412e-12. Epoch 322/1000 162/162 [==============================] - 0s 80us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 323/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 324/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 325/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 326/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 327/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 328/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 329/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 330/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 331/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00331: ReduceLROnPlateau reducing learning rate to 2.095475702679206e-12. Epoch 332/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 333/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 334/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 335/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 336/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 337/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 338/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 339/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 340/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 341/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00341: ReduceLROnPlateau reducing learning rate to 1.047737851339603e-12. Epoch 342/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 343/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 344/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 345/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 346/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 347/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 348/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 349/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 350/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 351/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00351: ReduceLROnPlateau reducing learning rate to 5.238689256698015e-13. Epoch 352/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 353/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 354/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 355/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 356/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 357/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 358/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 359/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 360/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 361/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00361: ReduceLROnPlateau reducing learning rate to 2.6193446283490074e-13. Epoch 362/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 363/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 364/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 365/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 366/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 367/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 368/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 369/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 370/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 371/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00371: ReduceLROnPlateau reducing learning rate to 1.3096723141745037e-13. Epoch 372/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 373/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 374/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 375/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 376/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 377/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 378/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 379/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 380/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 381/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00381: ReduceLROnPlateau reducing learning rate to 6.548361570872518e-14. Epoch 382/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 383/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 384/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 385/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 386/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 387/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 388/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 389/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 390/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 391/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00391: ReduceLROnPlateau reducing learning rate to 3.274180785436259e-14. Epoch 392/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 393/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 394/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 395/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 396/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 397/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 398/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 399/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 400/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 401/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00401: ReduceLROnPlateau reducing learning rate to 1.6370903927181296e-14. Epoch 402/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 403/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 404/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 405/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 406/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 407/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 408/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 409/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 410/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 411/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00411: ReduceLROnPlateau reducing learning rate to 8.185451963590648e-15. Epoch 412/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 413/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 414/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 415/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 416/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 417/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 418/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 419/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 420/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 421/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00421: ReduceLROnPlateau reducing learning rate to 4.092725981795324e-15. Epoch 422/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 423/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 424/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 425/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 426/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 427/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 428/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 429/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 430/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 431/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00431: ReduceLROnPlateau reducing learning rate to 2.046362990897662e-15. Epoch 432/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 433/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 434/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 435/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 436/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 437/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 438/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 439/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 440/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 441/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00441: ReduceLROnPlateau reducing learning rate to 1.023181495448831e-15. Epoch 442/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 443/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 444/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 445/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 446/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 447/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 448/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 449/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 450/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 451/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00451: ReduceLROnPlateau reducing learning rate to 5.115907477244155e-16. Epoch 452/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 453/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 454/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 455/1000 162/162 [==============================] - ETA: 0s - loss: 0.6148 - accuracy: 0.62 - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 456/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 457/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 458/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 459/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 460/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 461/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00461: ReduceLROnPlateau reducing learning rate to 2.5579537386220775e-16. Epoch 462/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 463/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 464/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 465/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 466/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 467/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 468/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 469/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 470/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 471/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00471: ReduceLROnPlateau reducing learning rate to 1.2789768693110388e-16. Epoch 472/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 473/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 474/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 475/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 476/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 477/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 478/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 479/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 480/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 481/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00481: ReduceLROnPlateau reducing learning rate to 6.394884346555194e-17. Epoch 482/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 483/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 484/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 485/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 486/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 487/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 488/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 489/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 490/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 491/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00491: ReduceLROnPlateau reducing learning rate to 3.197442173277597e-17. Epoch 492/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 493/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 494/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 495/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 496/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 497/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 498/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 499/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 500/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 501/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00501: ReduceLROnPlateau reducing learning rate to 1.5987210866387985e-17. Epoch 502/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 503/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 504/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 505/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 506/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 507/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 508/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 509/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 510/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 511/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00511: ReduceLROnPlateau reducing learning rate to 7.993605433193992e-18. Epoch 512/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 513/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 514/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 515/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 516/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 517/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 518/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 519/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 520/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 521/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00521: ReduceLROnPlateau reducing learning rate to 3.996802716596996e-18. Epoch 522/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 523/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 524/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 525/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 526/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 527/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 528/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 529/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 530/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 531/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00531: ReduceLROnPlateau reducing learning rate to 1.998401358298498e-18. Epoch 532/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 533/1000 162/162 [==============================] - ETA: 0s - loss: 0.6818 - accuracy: 0.53 - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 534/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 535/1000 162/162 [==============================] - ETA: 0s - loss: 0.5717 - accuracy: 0.59 - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 536/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 537/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 538/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 539/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 540/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 541/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00541: ReduceLROnPlateau reducing learning rate to 9.99200679149249e-19. Epoch 542/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 543/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 544/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 545/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 546/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 547/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 548/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 549/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 550/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 551/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00551: ReduceLROnPlateau reducing learning rate to 4.996003395746245e-19. Epoch 552/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 553/1000 162/162 [==============================] - 0s 167us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 554/1000 162/162 [==============================] - ETA: 0s - loss: 0.6048 - accuracy: 0.56 - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 555/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 556/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 557/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 558/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 559/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 560/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 561/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00561: ReduceLROnPlateau reducing learning rate to 2.4980016978731226e-19. Epoch 562/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 563/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 564/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 565/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 566/1000 162/162 [==============================] - 0s 234us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 567/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 568/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 569/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 570/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 571/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00571: ReduceLROnPlateau reducing learning rate to 1.2490008489365613e-19. Epoch 572/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 573/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 574/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 575/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 576/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 577/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 578/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 579/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 580/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 581/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00581: ReduceLROnPlateau reducing learning rate to 6.245004244682806e-20. Epoch 582/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 583/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 584/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 585/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 586/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 587/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 588/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 589/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 590/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 591/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00591: ReduceLROnPlateau reducing learning rate to 3.122502122341403e-20. Epoch 592/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 593/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 594/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 595/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 596/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 597/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 598/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 599/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 600/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 601/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00601: ReduceLROnPlateau reducing learning rate to 1.5612510611707016e-20. Epoch 602/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 603/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 604/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 605/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 606/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 607/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 608/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 609/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 610/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 611/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00611: ReduceLROnPlateau reducing learning rate to 7.806255305853508e-21. Epoch 612/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 613/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 614/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 615/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 616/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 617/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 618/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 619/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 620/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 621/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00621: ReduceLROnPlateau reducing learning rate to 3.903127652926754e-21. Epoch 622/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 623/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 624/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 625/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 626/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 627/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 628/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 629/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 630/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 631/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00631: ReduceLROnPlateau reducing learning rate to 1.951563826463377e-21. Epoch 632/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 633/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 634/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 635/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 636/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 637/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 638/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 639/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 640/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 641/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00641: ReduceLROnPlateau reducing learning rate to 9.757819132316885e-22. Epoch 642/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 643/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 644/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 645/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 646/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 647/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 648/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 649/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 650/1000 162/162 [==============================] - ETA: 0s - loss: 0.6500 - accuracy: 0.46 - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 651/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00651: ReduceLROnPlateau reducing learning rate to 4.878909566158443e-22. Epoch 652/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 653/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 654/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 655/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 656/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 657/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 658/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 659/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 660/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 661/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00661: ReduceLROnPlateau reducing learning rate to 2.4394547830792213e-22. Epoch 662/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 663/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 664/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 665/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 666/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 667/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 668/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 669/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 670/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 671/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00671: ReduceLROnPlateau reducing learning rate to 1.2197273915396106e-22. Epoch 672/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 673/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 674/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 675/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 676/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 677/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 678/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 679/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 680/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 681/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00681: ReduceLROnPlateau reducing learning rate to 6.098636957698053e-23. Epoch 682/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 683/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 684/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 685/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 686/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 687/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 688/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 689/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 690/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 691/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00691: ReduceLROnPlateau reducing learning rate to 3.0493184788490266e-23. Epoch 692/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 693/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 694/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 695/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 696/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 697/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 698/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 699/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 700/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 701/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00701: ReduceLROnPlateau reducing learning rate to 1.5246592394245133e-23. Epoch 702/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 703/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 704/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 705/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 706/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 707/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 708/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 709/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 710/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 711/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00711: ReduceLROnPlateau reducing learning rate to 7.623296197122566e-24. Epoch 712/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 713/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 714/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 715/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 716/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 717/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 718/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 719/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 720/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 721/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00721: ReduceLROnPlateau reducing learning rate to 3.811648098561283e-24. Epoch 722/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 723/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 724/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 725/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 726/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 727/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 728/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 729/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 730/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 731/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00731: ReduceLROnPlateau reducing learning rate to 1.9058240492806416e-24. Epoch 732/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 733/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 734/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 735/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 736/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 737/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 738/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 739/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 740/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 741/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00741: ReduceLROnPlateau reducing learning rate to 9.529120246403208e-25. Epoch 742/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 743/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 744/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 745/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 746/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 747/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 748/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 749/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 750/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 751/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00751: ReduceLROnPlateau reducing learning rate to 4.764560123201604e-25. Epoch 752/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 753/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 754/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 755/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 756/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 757/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 758/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 759/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 760/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 761/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00761: ReduceLROnPlateau reducing learning rate to 2.382280061600802e-25. Epoch 762/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 763/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 764/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 765/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 766/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 767/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 768/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 769/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 770/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 771/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00771: ReduceLROnPlateau reducing learning rate to 1.191140030800401e-25. Epoch 772/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 773/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 774/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 775/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 776/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 777/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 778/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 779/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 780/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 781/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00781: ReduceLROnPlateau reducing learning rate to 5.955700154002005e-26. Epoch 782/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 783/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 784/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 785/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 786/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 787/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 788/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 789/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 790/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 791/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00791: ReduceLROnPlateau reducing learning rate to 2.9778500770010025e-26. Epoch 792/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 793/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 794/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 795/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 796/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 797/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 798/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 799/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 800/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 801/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00801: ReduceLROnPlateau reducing learning rate to 1.4889250385005013e-26. Epoch 802/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 803/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 804/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 805/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 806/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 807/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 808/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 809/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 810/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 811/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00811: ReduceLROnPlateau reducing learning rate to 7.444625192502506e-27. Epoch 812/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 813/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 814/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 815/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 816/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 817/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 818/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 819/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 820/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 821/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00821: ReduceLROnPlateau reducing learning rate to 3.722312596251253e-27. Epoch 822/1000 162/162 [==============================] - 0s 93us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 823/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 824/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 825/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 826/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 827/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 828/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 829/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 830/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 831/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00831: ReduceLROnPlateau reducing learning rate to 1.8611562981256266e-27. Epoch 832/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 833/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 834/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 835/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 836/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 837/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 838/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 839/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 840/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 841/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00841: ReduceLROnPlateau reducing learning rate to 9.305781490628133e-28. Epoch 842/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 843/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 844/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 845/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 846/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 847/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 848/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 849/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 850/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 851/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00851: ReduceLROnPlateau reducing learning rate to 4.6528907453140665e-28. Epoch 852/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 853/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 854/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 855/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 856/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 857/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 858/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 859/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 860/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 861/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00861: ReduceLROnPlateau reducing learning rate to 2.3264453726570332e-28. Epoch 862/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 863/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 864/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 865/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 866/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 867/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 868/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 869/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 870/1000 162/162 [==============================] - 0s 142us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 871/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00871: ReduceLROnPlateau reducing learning rate to 1.1632226863285166e-28. Epoch 872/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 873/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 874/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 875/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 876/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 877/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 878/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 879/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 880/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 881/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00881: ReduceLROnPlateau reducing learning rate to 5.816113431642583e-29. Epoch 882/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 883/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 884/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 885/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 886/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 887/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 888/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 889/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 890/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 891/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00891: ReduceLROnPlateau reducing learning rate to 2.9080567158212915e-29. Epoch 892/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 893/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 894/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 895/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 896/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 897/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 898/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 899/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 900/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 901/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00901: ReduceLROnPlateau reducing learning rate to 1.4540283579106458e-29. Epoch 902/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 903/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 904/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 905/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 906/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 907/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 908/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 909/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 910/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 911/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00911: ReduceLROnPlateau reducing learning rate to 7.270141789553229e-30. Epoch 912/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 913/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 914/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 915/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 916/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 917/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 918/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 919/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 920/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 921/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00921: ReduceLROnPlateau reducing learning rate to 3.6350708947766144e-30. Epoch 922/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 923/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 924/1000 162/162 [==============================] - 0s 160us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 925/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 926/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 927/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 928/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 929/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 930/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 931/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00931: ReduceLROnPlateau reducing learning rate to 1.8175354473883072e-30. Epoch 932/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 933/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 934/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 935/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 936/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 937/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 938/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 939/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 940/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 941/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00941: ReduceLROnPlateau reducing learning rate to 9.087677236941536e-31. Epoch 942/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 943/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 944/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 945/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 946/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 947/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 948/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 949/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 950/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 951/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00951: ReduceLROnPlateau reducing learning rate to 4.543838618470768e-31. Epoch 952/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 953/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 954/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 955/1000 162/162 [==============================] - 0s 148us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 956/1000 162/162 [==============================] - 0s 136us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 957/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 958/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 959/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 960/1000 162/162 [==============================] - 0s 130us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 961/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00961: ReduceLROnPlateau reducing learning rate to 2.271919309235384e-31. Epoch 962/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 963/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 964/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 965/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 966/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 967/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 968/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 969/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 970/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 971/1000 162/162 [==============================] - ETA: 0s - loss: 0.6282 - accuracy: 0.56 - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00971: ReduceLROnPlateau reducing learning rate to 1.135959654617692e-31. Epoch 972/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 973/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 974/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 975/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 976/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 977/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 978/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 979/1000 162/162 [==============================] - 0s 117us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 980/1000 162/162 [==============================] - 0s 123us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 981/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00981: ReduceLROnPlateau reducing learning rate to 5.67979827308846e-32. Epoch 982/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 983/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 984/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 985/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 986/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 987/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 988/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 989/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 990/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 991/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 00991: ReduceLROnPlateau reducing learning rate to 2.83989913654423e-32. Epoch 992/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 993/1000 162/162 [==============================] - 0s 111us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 994/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 995/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 996/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 997/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 998/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 999/1000 162/162 [==============================] - 0s 105us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037 Epoch 1000/1000 162/162 [==============================] - 0s 99us/step - loss: 0.5800 - accuracy: 0.6605 - val_loss: 0.5577 - val_accuracy: 0.7037
In [127]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 1000)
In [128]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
54/54 [==============================] - 0s 74us/step test loss: 0.5576540055098357, test accuracy: 0.7037037014961243
In [129]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6190476190476191
In [130]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.08860759493670889 [[35 7] [ 9 3]]
KMeans¶
In [207]:
X
Out[207]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.420085 | -0.330086 | 0.476982 | 0.852458 | -0.881089 | -0.037777 | -0.607746 | -0.070107 | -0.654790 | -0.418599 | -0.802899 | -0.351189 | 1.002024 |
| 1 | -0.288523 | -1.545259 | -1.137074 | 1.113906 | 0.311278 | -1.566031 | -2.038457 | -0.322607 | -0.291288 | 1.094837 | 0.274704 | 1.777243 | 2.309084 |
| 2 | -0.424115 | 0.410085 | 0.838888 | 0.219947 | -0.375953 | -0.789691 | -0.335746 | -0.306896 | -0.962051 | 0.861800 | -0.453581 | 0.612777 | 0.646240 |
| 3 | -0.436131 | -1.584784 | 0.658995 | 0.766397 | -1.275956 | 1.369786 | 1.180080 | -2.004124 | -0.366360 | 0.460536 | -0.946577 | 0.167472 | 0.641078 |
| 4 | 0.000543 | 0.507984 | -0.978397 | -0.501031 | 0.347848 | 0.605158 | 0.571957 | -0.261476 | 0.046623 | -0.176286 | 0.124656 | 0.069963 | -0.657263 |
| 5 | 0.113948 | -0.640675 | -1.529179 | -1.089360 | 0.638256 | 1.024606 | 0.805429 | 0.597420 | -0.503707 | -0.866724 | 0.651241 | 1.674008 | 0.860657 |
| 6 | 0.807950 | -1.039887 | 2.968459 | 1.111222 | 0.521703 | 3.834393 | -0.154811 | -0.070639 | -0.395759 | -0.640282 | -0.725587 | -0.334158 | -0.618257 |
| 7 | -0.465670 | 0.173616 | 0.264449 | 2.278838 | 1.672258 | 0.689505 | 1.154324 | 1.417257 | 1.035726 | 0.390329 | -1.217981 | -1.123742 | 0.066049 |
| 8 | 0.235109 | -0.290452 | -0.928556 | 0.659411 | -0.320465 | 0.170612 | 0.154061 | 0.895453 | 0.758427 | -0.408150 | -1.056703 | 0.829210 | 2.099893 |
| 9 | 0.296153 | 1.351512 | -0.018047 | 0.386276 | -0.911469 | 0.796022 | -0.567061 | -0.077659 | 0.096104 | -0.635810 | -0.597365 | -1.005655 | 0.087116 |
| 10 | 1.304720 | 0.208245 | 0.583743 | 1.972234 | -0.030334 | 0.162684 | -0.208981 | 0.601801 | -1.515513 | -3.719401 | -0.895641 | 0.233657 | -1.060053 |
| 11 | 1.288979 | -0.851636 | -0.002189 | -0.753502 | -0.288197 | -1.486411 | -0.866242 | 0.068441 | -0.112261 | 0.084143 | 0.404865 | -1.121813 | 0.068799 |
| 12 | 0.802167 | 1.125872 | 0.181614 | -0.054998 | 0.125978 | -0.118093 | -0.204100 | -0.240702 | 0.725047 | -0.060416 | 0.610221 | 0.097037 | 0.758946 |
| 13 | -0.570677 | -0.903477 | 0.077307 | 1.133679 | -0.194704 | -2.680079 | -1.869617 | 0.413585 | -0.778149 | 0.810256 | 0.538818 | 1.572772 | 2.002755 |
| 14 | 0.390585 | 1.185091 | 1.060521 | -0.143387 | 0.154017 | -0.184047 | -0.178747 | -1.592611 | 0.195876 | 0.676819 | 1.052625 | -0.193902 | 0.538474 |
| 15 | -0.294008 | 1.505226 | 0.525191 | 0.408188 | 0.012660 | 0.846051 | 0.444150 | -0.303052 | 0.701059 | 0.270748 | -0.345435 | -0.554816 | -0.663403 |
| 16 | 0.504074 | 0.589989 | 0.264178 | -0.853628 | -1.595569 | -0.010760 | 0.220518 | 0.048998 | 0.250424 | 0.796887 | -0.099404 | -1.017561 | -0.397998 |
| 17 | -0.894319 | -1.371343 | -0.705746 | 0.481594 | -0.141646 | -0.042173 | -0.245037 | 0.348273 | 0.688880 | -0.027577 | -0.865534 | -1.210148 | 0.271172 |
| 18 | 0.851995 | 0.048612 | 0.066938 | 0.223581 | -0.911164 | -1.152240 | -0.070054 | -0.416772 | -0.480978 | -0.106632 | -0.231062 | -0.850191 | 0.896367 |
| 19 | -0.660027 | -0.598339 | -0.142295 | 1.087024 | -2.982385 | -0.973575 | 0.199468 | -2.163834 | -0.508427 | 1.292035 | -1.534447 | -0.433825 | 0.590292 |
| 20 | -0.566037 | -2.086297 | 0.061463 | -0.482666 | -1.080805 | 0.084488 | -0.543972 | 0.386352 | -0.614930 | -0.730628 | -0.994692 | 0.783494 | 0.132430 |
| 21 | 1.221627 | -0.027554 | 0.188106 | -0.974894 | 0.005596 | 1.102640 | -0.048800 | -0.490088 | -1.842707 | 0.102032 | 0.157223 | 0.601727 | 1.366630 |
| 22 | -1.183193 | -0.312675 | -0.568276 | -0.639410 | -0.208614 | 0.261554 | -0.216256 | 0.261899 | 0.331089 | 0.345426 | 0.485643 | 0.504079 | 0.397818 |
| 23 | -0.801169 | 0.116605 | -0.579690 | 0.362276 | 0.606309 | -0.570757 | -1.548593 | 0.718826 | 2.916797 | 2.075668 | 0.518297 | -0.400812 | -0.252481 |
| 24 | -0.891340 | 0.309727 | -0.842676 | 0.310338 | 0.577762 | -0.334300 | -1.473445 | 0.513897 | 2.805987 | 2.062959 | 0.635321 | -0.248494 | -0.258253 |
| 25 | -0.130263 | 1.334103 | 0.730042 | 0.678688 | -0.720646 | -0.666357 | 0.000279 | -0.436292 | 0.339978 | 1.010170 | 0.805445 | 0.781825 | -0.399769 |
| 26 | -1.602028 | 1.309495 | 0.611484 | 0.700892 | -0.666054 | -0.863765 | -0.615702 | -0.509079 | 0.601651 | 0.871639 | -0.301423 | -1.640656 | -0.131728 |
| 27 | -0.525208 | 1.525178 | -0.030851 | -0.074993 | 0.453039 | 1.307113 | 0.452591 | -0.220734 | 0.853076 | 0.349373 | -0.297713 | 0.168680 | -0.091233 |
| 28 | 0.009250 | -0.578236 | -0.700373 | -1.085258 | -0.295803 | -0.883641 | 2.594736 | 0.321904 | 0.390719 | -1.529163 | -0.511611 | -1.805971 | 0.998148 |
| 29 | 0.280166 | -0.217504 | -2.181350 | 0.187090 | 0.622591 | -2.686573 | -0.214488 | 1.438992 | 1.094095 | -1.319265 | 0.002128 | -3.970580 | -0.678777 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | -0.713223 | -0.165097 | 0.170953 | -0.307601 | 1.152684 | -0.045434 | -0.432254 | 0.388447 | -0.400713 | -0.024795 | -0.444954 | 0.651175 | 0.856965 |
| 187 | 0.077144 | -0.527913 | 0.300841 | -0.007145 | -0.844229 | -0.260791 | -1.196957 | 0.080170 | 0.801803 | 0.095003 | -0.099609 | 0.698672 | -0.673575 |
| 188 | -0.818527 | -0.256716 | 0.589448 | -0.243361 | 0.027958 | 0.218148 | 0.278190 | 0.579538 | 0.547728 | 1.017426 | 0.085377 | -1.012860 | -1.117093 |
| 189 | 0.078094 | -0.693228 | -0.177029 | 0.143179 | -2.181764 | -1.150077 | -0.455986 | 2.342589 | 0.559829 | -0.323766 | 1.119820 | 0.558999 | 1.029247 |
| 190 | 0.473118 | -0.619480 | -0.613859 | -1.390025 | -2.181316 | -1.933866 | -1.862714 | 2.547096 | 0.230172 | -1.472410 | 0.795467 | 0.361148 | 0.443935 |
| 191 | -0.725236 | 0.975099 | 1.683062 | -0.427378 | 1.353092 | -0.378540 | 0.888469 | 0.944767 | 0.523458 | -0.783620 | 0.384682 | 0.536515 | 0.242834 |
| 192 | -0.098130 | 0.349984 | 0.651382 | 0.850819 | 0.452135 | 0.155512 | 0.327726 | 0.884508 | -0.713577 | -0.090647 | 0.810323 | 0.862330 | 0.315889 |
| 193 | -0.629175 | 0.598836 | 0.560497 | 0.331765 | 0.692832 | 0.040925 | 0.021748 | -0.346042 | -0.072500 | -0.054767 | 1.405269 | 0.652914 | 0.119071 |
| 194 | -0.593283 | -1.762364 | -1.292266 | -0.014741 | 0.048404 | 1.051485 | 1.378317 | 0.769360 | -0.538726 | -0.881937 | -0.734744 | -0.034792 | 0.551083 |
| 195 | -0.220725 | -0.706286 | -0.558429 | -0.543495 | -0.762451 | -0.724549 | 0.033926 | 0.427916 | 0.381670 | 0.691530 | 1.157624 | 0.554176 | 0.085881 |
| 196 | 0.542442 | -0.314573 | -0.389836 | 1.340826 | -0.685860 | 1.357357 | -0.180731 | -0.134883 | 1.542849 | -0.544367 | -1.675576 | 0.661188 | -1.008124 |
| 197 | 0.881881 | 0.686691 | 1.412427 | 0.067865 | -0.239689 | 0.560162 | 0.252073 | -1.596612 | 0.028578 | 0.915895 | -0.464715 | 0.139984 | 0.221738 |
| 198 | 0.056082 | 0.354969 | -0.320369 | -0.059290 | -0.029903 | -0.037445 | 0.463365 | 1.036375 | -0.996880 | -0.421318 | -0.105980 | 0.153928 | -0.138210 |
| 199 | -0.069357 | 0.007801 | -0.207830 | -0.057174 | -0.226654 | 0.215090 | -0.377980 | 0.770378 | 0.045795 | 0.749183 | -0.041079 | 0.507759 | 0.857287 |
| 200 | 0.469206 | -0.809604 | -0.887115 | -0.746687 | -1.496004 | 0.062379 | -0.191488 | 2.511061 | 0.807064 | 1.982195 | -1.624512 | 1.054232 | -1.213713 |
| 201 | -0.020242 | -0.015071 | -0.121820 | -0.371781 | -0.501178 | 0.021533 | -0.183053 | 0.947258 | 0.121773 | 0.744517 | -0.020175 | 0.696027 | 1.176563 |
| 202 | -0.850319 | -0.167766 | 1.753294 | -0.158230 | 1.464227 | -1.912449 | 0.756675 | -1.829681 | 1.018012 | 0.323314 | -1.687154 | 0.771247 | -1.800694 |
| 203 | -1.592416 | 0.518318 | -0.040842 | -0.026786 | 0.407023 | 0.751642 | 0.136831 | -0.519756 | -0.647307 | -0.663112 | -0.108425 | -0.070893 | 0.564588 |
| 204 | -1.525605 | 0.408122 | -0.068239 | -0.027937 | 0.579957 | 0.945646 | 0.028783 | -0.684904 | -0.605690 | -0.554342 | 0.077226 | -0.032084 | 0.772622 |
| 205 | 0.460758 | -0.673974 | -1.175590 | -0.042902 | -0.354361 | 0.110479 | -1.172032 | -0.586283 | 0.068460 | -0.294180 | 0.791941 | 0.725567 | -0.557938 |
| 206 | -0.654520 | -0.315921 | 0.582093 | 0.860981 | -2.255891 | 0.280515 | -0.167860 | -0.060341 | 0.774684 | -0.965387 | -0.272312 | 0.965091 | -2.037667 |
| 207 | -0.746404 | -0.272009 | 0.915424 | 0.849620 | -2.868535 | 0.209161 | -0.279975 | -0.187035 | 1.111337 | -0.940798 | -0.010679 | 1.676010 | -2.095188 |
| 208 | -0.406754 | 0.539656 | 0.644480 | 1.231427 | 0.299700 | -0.877891 | -0.636255 | -0.696446 | 0.413124 | 0.006952 | -0.652237 | -0.705710 | -0.605430 |
| 209 | 0.239265 | 2.028215 | -0.262443 | -0.154385 | -0.408932 | -1.139249 | 0.361170 | -0.142219 | 1.390750 | -0.247556 | -1.030608 | 0.769854 | 0.315698 |
| 210 | -0.275298 | 0.271105 | 0.428430 | 1.445529 | -0.169781 | -1.352517 | -0.388072 | -0.917276 | -0.332714 | -0.809595 | -0.907645 | -1.645002 | -0.894372 |
| 211 | 0.151626 | 1.433998 | 2.658181 | -0.681422 | -0.210581 | 1.432182 | 0.997691 | -2.562925 | -0.348259 | 0.825118 | 2.785329 | 0.897498 | 1.962963 |
| 212 | 0.681872 | 0.671929 | 0.127597 | -0.665368 | 0.282250 | 0.541495 | 1.003312 | -0.196048 | -0.380222 | -1.300609 | 1.441472 | 1.382278 | 0.192263 |
| 213 | 0.706389 | 0.041252 | -0.040976 | -0.311682 | -0.096455 | 0.762413 | -0.450937 | -0.134575 | -0.102032 | 0.750741 | -0.104707 | -0.048909 | 0.682180 |
| 214 | -1.588263 | 0.426822 | -0.788645 | -0.221386 | 0.983329 | 1.558246 | 1.332190 | 0.532878 | -0.647525 | -0.023726 | -0.959608 | 1.274829 | 2.334968 |
| 215 | -0.774487 | -1.852850 | -0.785635 | -0.149473 | -0.236634 | 0.462963 | 1.000185 | -0.047619 | -0.310695 | -0.127651 | 0.490345 | 0.233474 | 0.163391 |
216 rows × 13 columns
In [208]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[208]:
[2808.0, 2548.3417429105866, 2378.335995807065, 2256.0960796172503, 2169.4597299478096, 2080.2739448933166, 2001.1226520766459, 1918.5739069931988, 1880.9859014602027, 1796.9350644070514, 1787.591551933858, 1721.2471823270262, 1689.3458120683304, 1657.5154964943713]
In [209]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[209]:
[<matplotlib.lines.Line2D at 0x1b8293108d0>]
K=2
In [210]:
kmeans_mfcc = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[210]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [211]:
kmeans_mfcc.labels_
Out[211]:
array([0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,
1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0,
1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0,
0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1])
In [212]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[212]:
array([0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,
1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0,
1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0,
0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1])
In [213]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [214]:
X
Out[214]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.420085 | -0.330086 | 0.476982 | 0.852458 | -0.881089 | -0.037777 | -0.607746 | -0.070107 | -0.654790 | -0.418599 | -0.802899 | -0.351189 | 1.002024 | 0 | 0 |
| 1 | -0.288523 | -1.545259 | -1.137074 | 1.113906 | 0.311278 | -1.566031 | -2.038457 | -0.322607 | -0.291288 | 1.094837 | 0.274704 | 1.777243 | 2.309084 | 0 | 0 |
| 2 | -0.424115 | 0.410085 | 0.838888 | 0.219947 | -0.375953 | -0.789691 | -0.335746 | -0.306896 | -0.962051 | 0.861800 | -0.453581 | 0.612777 | 0.646240 | 0 | 0 |
| 3 | -0.436131 | -1.584784 | 0.658995 | 0.766397 | -1.275956 | 1.369786 | 1.180080 | -2.004124 | -0.366360 | 0.460536 | -0.946577 | 0.167472 | 0.641078 | 0 | 0 |
| 4 | 0.000543 | 0.507984 | -0.978397 | -0.501031 | 0.347848 | 0.605158 | 0.571957 | -0.261476 | 0.046623 | -0.176286 | 0.124656 | 0.069963 | -0.657263 | 1 | 0 |
| 5 | 0.113948 | -0.640675 | -1.529179 | -1.089360 | 0.638256 | 1.024606 | 0.805429 | 0.597420 | -0.503707 | -0.866724 | 0.651241 | 1.674008 | 0.860657 | 1 | 0 |
| 6 | 0.807950 | -1.039887 | 2.968459 | 1.111222 | 0.521703 | 3.834393 | -0.154811 | -0.070639 | -0.395759 | -0.640282 | -0.725587 | -0.334158 | -0.618257 | 0 | 0 |
| 7 | -0.465670 | 0.173616 | 0.264449 | 2.278838 | 1.672258 | 0.689505 | 1.154324 | 1.417257 | 1.035726 | 0.390329 | -1.217981 | -1.123742 | 0.066049 | 0 | 0 |
| 8 | 0.235109 | -0.290452 | -0.928556 | 0.659411 | -0.320465 | 0.170612 | 0.154061 | 0.895453 | 0.758427 | -0.408150 | -1.056703 | 0.829210 | 2.099893 | 1 | 0 |
| 9 | 0.296153 | 1.351512 | -0.018047 | 0.386276 | -0.911469 | 0.796022 | -0.567061 | -0.077659 | 0.096104 | -0.635810 | -0.597365 | -1.005655 | 0.087116 | 0 | 0 |
| 10 | 1.304720 | 0.208245 | 0.583743 | 1.972234 | -0.030334 | 0.162684 | -0.208981 | 0.601801 | -1.515513 | -3.719401 | -0.895641 | 0.233657 | -1.060053 | 0 | 0 |
| 11 | 1.288979 | -0.851636 | -0.002189 | -0.753502 | -0.288197 | -1.486411 | -0.866242 | 0.068441 | -0.112261 | 0.084143 | 0.404865 | -1.121813 | 0.068799 | 1 | 0 |
| 12 | 0.802167 | 1.125872 | 0.181614 | -0.054998 | 0.125978 | -0.118093 | -0.204100 | -0.240702 | 0.725047 | -0.060416 | 0.610221 | 0.097037 | 0.758946 | 0 | 0 |
| 13 | -0.570677 | -0.903477 | 0.077307 | 1.133679 | -0.194704 | -2.680079 | -1.869617 | 0.413585 | -0.778149 | 0.810256 | 0.538818 | 1.572772 | 2.002755 | 0 | 0 |
| 14 | 0.390585 | 1.185091 | 1.060521 | -0.143387 | 0.154017 | -0.184047 | -0.178747 | -1.592611 | 0.195876 | 0.676819 | 1.052625 | -0.193902 | 0.538474 | 0 | 0 |
| 15 | -0.294008 | 1.505226 | 0.525191 | 0.408188 | 0.012660 | 0.846051 | 0.444150 | -0.303052 | 0.701059 | 0.270748 | -0.345435 | -0.554816 | -0.663403 | 0 | 0 |
| 16 | 0.504074 | 0.589989 | 0.264178 | -0.853628 | -1.595569 | -0.010760 | 0.220518 | 0.048998 | 0.250424 | 0.796887 | -0.099404 | -1.017561 | -0.397998 | 1 | 0 |
| 17 | -0.894319 | -1.371343 | -0.705746 | 0.481594 | -0.141646 | -0.042173 | -0.245037 | 0.348273 | 0.688880 | -0.027577 | -0.865534 | -1.210148 | 0.271172 | 1 | 0 |
| 18 | 0.851995 | 0.048612 | 0.066938 | 0.223581 | -0.911164 | -1.152240 | -0.070054 | -0.416772 | -0.480978 | -0.106632 | -0.231062 | -0.850191 | 0.896367 | 0 | 0 |
| 19 | -0.660027 | -0.598339 | -0.142295 | 1.087024 | -2.982385 | -0.973575 | 0.199468 | -2.163834 | -0.508427 | 1.292035 | -1.534447 | -0.433825 | 0.590292 | 0 | 0 |
| 20 | -0.566037 | -2.086297 | 0.061463 | -0.482666 | -1.080805 | 0.084488 | -0.543972 | 0.386352 | -0.614930 | -0.730628 | -0.994692 | 0.783494 | 0.132430 | 1 | 0 |
| 21 | 1.221627 | -0.027554 | 0.188106 | -0.974894 | 0.005596 | 1.102640 | -0.048800 | -0.490088 | -1.842707 | 0.102032 | 0.157223 | 0.601727 | 1.366630 | 0 | 0 |
| 22 | -1.183193 | -0.312675 | -0.568276 | -0.639410 | -0.208614 | 0.261554 | -0.216256 | 0.261899 | 0.331089 | 0.345426 | 0.485643 | 0.504079 | 0.397818 | 1 | 0 |
| 23 | -0.801169 | 0.116605 | -0.579690 | 0.362276 | 0.606309 | -0.570757 | -1.548593 | 0.718826 | 2.916797 | 2.075668 | 0.518297 | -0.400812 | -0.252481 | 1 | 0 |
| 24 | -0.891340 | 0.309727 | -0.842676 | 0.310338 | 0.577762 | -0.334300 | -1.473445 | 0.513897 | 2.805987 | 2.062959 | 0.635321 | -0.248494 | -0.258253 | 1 | 0 |
| 25 | -0.130263 | 1.334103 | 0.730042 | 0.678688 | -0.720646 | -0.666357 | 0.000279 | -0.436292 | 0.339978 | 1.010170 | 0.805445 | 0.781825 | -0.399769 | 0 | 0 |
| 26 | -1.602028 | 1.309495 | 0.611484 | 0.700892 | -0.666054 | -0.863765 | -0.615702 | -0.509079 | 0.601651 | 0.871639 | -0.301423 | -1.640656 | -0.131728 | 0 | 0 |
| 27 | -0.525208 | 1.525178 | -0.030851 | -0.074993 | 0.453039 | 1.307113 | 0.452591 | -0.220734 | 0.853076 | 0.349373 | -0.297713 | 0.168680 | -0.091233 | 0 | 0 |
| 28 | 0.009250 | -0.578236 | -0.700373 | -1.085258 | -0.295803 | -0.883641 | 2.594736 | 0.321904 | 0.390719 | -1.529163 | -0.511611 | -1.805971 | 0.998148 | 1 | 0 |
| 29 | 0.280166 | -0.217504 | -2.181350 | 0.187090 | 0.622591 | -2.686573 | -0.214488 | 1.438992 | 1.094095 | -1.319265 | 0.002128 | -3.970580 | -0.678777 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | -0.713223 | -0.165097 | 0.170953 | -0.307601 | 1.152684 | -0.045434 | -0.432254 | 0.388447 | -0.400713 | -0.024795 | -0.444954 | 0.651175 | 0.856965 | 0 | 1 |
| 187 | 0.077144 | -0.527913 | 0.300841 | -0.007145 | -0.844229 | -0.260791 | -1.196957 | 0.080170 | 0.801803 | 0.095003 | -0.099609 | 0.698672 | -0.673575 | 1 | 1 |
| 188 | -0.818527 | -0.256716 | 0.589448 | -0.243361 | 0.027958 | 0.218148 | 0.278190 | 0.579538 | 0.547728 | 1.017426 | 0.085377 | -1.012860 | -1.117093 | 1 | 1 |
| 189 | 0.078094 | -0.693228 | -0.177029 | 0.143179 | -2.181764 | -1.150077 | -0.455986 | 2.342589 | 0.559829 | -0.323766 | 1.119820 | 0.558999 | 1.029247 | 1 | 1 |
| 190 | 0.473118 | -0.619480 | -0.613859 | -1.390025 | -2.181316 | -1.933866 | -1.862714 | 2.547096 | 0.230172 | -1.472410 | 0.795467 | 0.361148 | 0.443935 | 1 | 1 |
| 191 | -0.725236 | 0.975099 | 1.683062 | -0.427378 | 1.353092 | -0.378540 | 0.888469 | 0.944767 | 0.523458 | -0.783620 | 0.384682 | 0.536515 | 0.242834 | 0 | 1 |
| 192 | -0.098130 | 0.349984 | 0.651382 | 0.850819 | 0.452135 | 0.155512 | 0.327726 | 0.884508 | -0.713577 | -0.090647 | 0.810323 | 0.862330 | 0.315889 | 0 | 1 |
| 193 | -0.629175 | 0.598836 | 0.560497 | 0.331765 | 0.692832 | 0.040925 | 0.021748 | -0.346042 | -0.072500 | -0.054767 | 1.405269 | 0.652914 | 0.119071 | 0 | 1 |
| 194 | -0.593283 | -1.762364 | -1.292266 | -0.014741 | 0.048404 | 1.051485 | 1.378317 | 0.769360 | -0.538726 | -0.881937 | -0.734744 | -0.034792 | 0.551083 | 1 | 1 |
| 195 | -0.220725 | -0.706286 | -0.558429 | -0.543495 | -0.762451 | -0.724549 | 0.033926 | 0.427916 | 0.381670 | 0.691530 | 1.157624 | 0.554176 | 0.085881 | 1 | 1 |
| 196 | 0.542442 | -0.314573 | -0.389836 | 1.340826 | -0.685860 | 1.357357 | -0.180731 | -0.134883 | 1.542849 | -0.544367 | -1.675576 | 0.661188 | -1.008124 | 1 | 1 |
| 197 | 0.881881 | 0.686691 | 1.412427 | 0.067865 | -0.239689 | 0.560162 | 0.252073 | -1.596612 | 0.028578 | 0.915895 | -0.464715 | 0.139984 | 0.221738 | 0 | 1 |
| 198 | 0.056082 | 0.354969 | -0.320369 | -0.059290 | -0.029903 | -0.037445 | 0.463365 | 1.036375 | -0.996880 | -0.421318 | -0.105980 | 0.153928 | -0.138210 | 1 | 1 |
| 199 | -0.069357 | 0.007801 | -0.207830 | -0.057174 | -0.226654 | 0.215090 | -0.377980 | 0.770378 | 0.045795 | 0.749183 | -0.041079 | 0.507759 | 0.857287 | 0 | 1 |
| 200 | 0.469206 | -0.809604 | -0.887115 | -0.746687 | -1.496004 | 0.062379 | -0.191488 | 2.511061 | 0.807064 | 1.982195 | -1.624512 | 1.054232 | -1.213713 | 1 | 1 |
| 201 | -0.020242 | -0.015071 | -0.121820 | -0.371781 | -0.501178 | 0.021533 | -0.183053 | 0.947258 | 0.121773 | 0.744517 | -0.020175 | 0.696027 | 1.176563 | 1 | 1 |
| 202 | -0.850319 | -0.167766 | 1.753294 | -0.158230 | 1.464227 | -1.912449 | 0.756675 | -1.829681 | 1.018012 | 0.323314 | -1.687154 | 0.771247 | -1.800694 | 0 | 1 |
| 203 | -1.592416 | 0.518318 | -0.040842 | -0.026786 | 0.407023 | 0.751642 | 0.136831 | -0.519756 | -0.647307 | -0.663112 | -0.108425 | -0.070893 | 0.564588 | 0 | 1 |
| 204 | -1.525605 | 0.408122 | -0.068239 | -0.027937 | 0.579957 | 0.945646 | 0.028783 | -0.684904 | -0.605690 | -0.554342 | 0.077226 | -0.032084 | 0.772622 | 0 | 1 |
| 205 | 0.460758 | -0.673974 | -1.175590 | -0.042902 | -0.354361 | 0.110479 | -1.172032 | -0.586283 | 0.068460 | -0.294180 | 0.791941 | 0.725567 | -0.557938 | 1 | 1 |
| 206 | -0.654520 | -0.315921 | 0.582093 | 0.860981 | -2.255891 | 0.280515 | -0.167860 | -0.060341 | 0.774684 | -0.965387 | -0.272312 | 0.965091 | -2.037667 | 1 | 1 |
| 207 | -0.746404 | -0.272009 | 0.915424 | 0.849620 | -2.868535 | 0.209161 | -0.279975 | -0.187035 | 1.111337 | -0.940798 | -0.010679 | 1.676010 | -2.095188 | 1 | 1 |
| 208 | -0.406754 | 0.539656 | 0.644480 | 1.231427 | 0.299700 | -0.877891 | -0.636255 | -0.696446 | 0.413124 | 0.006952 | -0.652237 | -0.705710 | -0.605430 | 0 | 1 |
| 209 | 0.239265 | 2.028215 | -0.262443 | -0.154385 | -0.408932 | -1.139249 | 0.361170 | -0.142219 | 1.390750 | -0.247556 | -1.030608 | 0.769854 | 0.315698 | 0 | 1 |
| 210 | -0.275298 | 0.271105 | 0.428430 | 1.445529 | -0.169781 | -1.352517 | -0.388072 | -0.917276 | -0.332714 | -0.809595 | -0.907645 | -1.645002 | -0.894372 | 0 | 1 |
| 211 | 0.151626 | 1.433998 | 2.658181 | -0.681422 | -0.210581 | 1.432182 | 0.997691 | -2.562925 | -0.348259 | 0.825118 | 2.785329 | 0.897498 | 1.962963 | 0 | 1 |
| 212 | 0.681872 | 0.671929 | 0.127597 | -0.665368 | 0.282250 | 0.541495 | 1.003312 | -0.196048 | -0.380222 | -1.300609 | 1.441472 | 1.382278 | 0.192263 | 0 | 1 |
| 213 | 0.706389 | 0.041252 | -0.040976 | -0.311682 | -0.096455 | 0.762413 | -0.450937 | -0.134575 | -0.102032 | 0.750741 | -0.104707 | -0.048909 | 0.682180 | 0 | 1 |
| 214 | -1.588263 | 0.426822 | -0.788645 | -0.221386 | 0.983329 | 1.558246 | 1.332190 | 0.532878 | -0.647525 | -0.023726 | -0.959608 | 1.274829 | 2.334968 | 0 | 1 |
| 215 | -0.774487 | -1.852850 | -0.785635 | -0.149473 | -0.236634 | 0.462963 | 1.000185 | -0.047619 | -0.310695 | -0.127651 | 0.490345 | 0.233474 | 0.163391 | 1 | 1 |
216 rows × 15 columns
In [215]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[215]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b829357fd0>
In [252]:
import itertools
from sklearn.metrics import confusion_matrix
Combinations¶
In [ ]:
two_combs = list(itertools.combinations(range(len(companies)), 2))
for comb in two_combs:
print('## '+companies[comb[0]].upper()+' y '+companies[comb[1]].upper())
X = df_n_ps_std_mfcc[comb[0]].append(df_n_ps_std_mfcc[comb[1]])
y = df_n_ps[comb[0]]['chosen'].append(df_n_ps[comb[1]]['chosen'])
print(X.head())
X_train, X_test, y_train, y_test = train_test_split(X, y)
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
if grid.best_params_['activation'] == 'logistic':
grid.best_params_['activation'] = 'sigmoid'
input_tensor = Input(shape = (n0,))
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
print(model.summary())
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), verbose=0,
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=0
)
]
)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print("epochs: "+str(len(acc)))
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
## ARTE FRANCÉS y CLUB DE BANQUEROS Y EMPRESARIOS
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.297583 1.225637 -0.367641 0.606499 0.072373
1 0.637676 -1.507256 -1.572737 -0.954161 -0.857425
2 2.236730 -0.319414 0.669910 -1.918119 -0.820882
3 0.662077 -0.381499 0.111981 -1.743808 -1.317593
4 0.736502 0.112932 -0.065024 -1.049458 -0.408043
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 -2.029620 0.791469 0.752018 2.268802 -1.383289
1 0.327005 0.816764 0.214245 0.241703 0.637066
2 -2.379333 -1.570021 -2.755344 -2.150610 -2.528577
3 -1.348534 -0.627198 -1.629882 -2.075974 -1.248765
4 -0.437499 0.090831 -0.852983 -1.922491 -0.284365
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 0.548279 1.903211 -1.011470
1 1.601538 0.300317 -0.466779
2 -0.877081 -0.522248 -1.429911
3 -1.126014 -1.316359 -1.126174
4 0.210624 -0.032122 -0.700183
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.008, 'max_iter': 2000}, que permiten obtener un Accuracy de 78.22% y un Kappa del 43.55
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_1 (Dense) (None, 30) 420
_________________________________________________________________
dense_2 (Dense) (None, 1) 31
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
143/143 [==============================] - 0s 42us/step
test loss: 0.8208914674245394, test accuracy: 0.6643356680870056
AUC ROC: 0.6193208990913438
Kappa: 0.16719242902208198
[[79 23]
[25 16]]
## ARTE FRANCÉS y GRAMMA
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.297583 1.225637 -0.367641 0.606499 0.072373
1 0.637676 -1.507256 -1.572737 -0.954161 -0.857425
2 2.236730 -0.319414 0.669910 -1.918119 -0.820882
3 0.662077 -0.381499 0.111981 -1.743808 -1.317593
4 0.736502 0.112932 -0.065024 -1.049458 -0.408043
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 -2.029620 0.791469 0.752018 2.268802 -1.383289
1 0.327005 0.816764 0.214245 0.241703 0.637066
2 -2.379333 -1.570021 -2.755344 -2.150610 -2.528577
3 -1.348534 -0.627198 -1.629882 -2.075974 -1.248765
4 -0.437499 0.090831 -0.852983 -1.922491 -0.284365
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 0.548279 1.903211 -1.011470
1 1.601538 0.300317 -0.466779
2 -0.877081 -0.522248 -1.429911
3 -1.126014 -1.316359 -1.126174
4 0.210624 -0.032122 -0.700183
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet. % self.max_iter, ConvergenceWarning)
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 20, 10), 'learning_rate_init': 0.008, 'max_iter': 20}, que permiten obtener un Accuracy de 73.87% y un Kappa del 21.99
Model: "model_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_3 (Dense) (None, 30) 420
_________________________________________________________________
dense_4 (Dense) (None, 20) 620
_________________________________________________________________
dense_5 (Dense) (None, 10) 210
_________________________________________________________________
dense_6 (Dense) (None, 1) 11
=================================================================
Total params: 1,261
Trainable params: 1,261
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
133/133 [==============================] - 0s 68us/step
test loss: 0.9437371667166402, test accuracy: 0.6992481350898743
AUC ROC: 0.6608910891089109
Kappa: 0.22651933701657456
[[78 23]
[17 15]]
## ARTE FRANCÉS y HOTEL MARRAKECH
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.297583 1.225637 -0.367641 0.606499 0.072373
1 0.637676 -1.507256 -1.572737 -0.954161 -0.857425
2 2.236730 -0.319414 0.669910 -1.918119 -0.820882
3 0.662077 -0.381499 0.111981 -1.743808 -1.317593
4 0.736502 0.112932 -0.065024 -1.049458 -0.408043
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 -2.029620 0.791469 0.752018 2.268802 -1.383289
1 0.327005 0.816764 0.214245 0.241703 0.637066
2 -2.379333 -1.570021 -2.755344 -2.150610 -2.528577
3 -1.348534 -0.627198 -1.629882 -2.075974 -1.248765
4 -0.437499 0.090831 -0.852983 -1.922491 -0.284365
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 0.548279 1.903211 -1.011470
1 1.601538 0.300317 -0.466779
2 -0.877081 -0.522248 -1.429911
3 -1.126014 -1.316359 -1.126174
4 0.210624 -0.032122 -0.700183
In [253]:
from IPython.display import display, Markdown, Latex
two_combs = list(itertools.combinations(range(len(companies)), 2))
for comb in two_combs[2:]:
display(Markdown('## '+companies[comb[0]]+' y '+companies[comb[1]]))
X = df_n_ps_std_mfcc[comb[0]].append(df_n_ps_std_mfcc[comb[1]])
y = df_n_ps[comb[0]]['chosen'].append(df_n_ps[comb[1]]['chosen'])
print(X.head())
X_train, X_test, y_train, y_test = train_test_split(X, y)
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
input_tensor = Input(shape = (n0,))
hidden_outputs = [input_tensor]
if grid.best_params_['activation'] == 'logistic':
grid.best_params_['activation'] = 'sigmoid'
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
print(model.summary())
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), verbose=0,
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=0
)
]
)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print("epochs: "+str(len(acc)))
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
## ARTE FRANCÉS y HOTEL MARRAKECH
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.297583 1.225637 -0.367641 0.606499 0.072373
1 0.637676 -1.507256 -1.572737 -0.954161 -0.857425
2 2.236730 -0.319414 0.669910 -1.918119 -0.820882
3 0.662077 -0.381499 0.111981 -1.743808 -1.317593
4 0.736502 0.112932 -0.065024 -1.049458 -0.408043
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 -2.029620 0.791469 0.752018 2.268802 -1.383289
1 0.327005 0.816764 0.214245 0.241703 0.637066
2 -2.379333 -1.570021 -2.755344 -2.150610 -2.528577
3 -1.348534 -0.627198 -1.629882 -2.075974 -1.248765
4 -0.437499 0.090831 -0.852983 -1.922491 -0.284365
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 0.548279 1.903211 -1.011470
1 1.601538 0.300317 -0.466779
2 -0.877081 -0.522248 -1.429911
3 -1.126014 -1.316359 -1.126174
4 0.210624 -0.032122 -0.700183
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 30), 'learning_rate_init': 0.02, 'max_iter': 200}, que permiten obtener un Accuracy de 74.71% y un Kappa del 41.29
Model: "model_13"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_14 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_38 (Dense) (None, 30) 420
_________________________________________________________________
dense_39 (Dense) (None, 30) 930
_________________________________________________________________
dense_40 (Dense) (None, 1) 31
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 200
115/115 [==============================] - 0s 52us/step
test loss: 1.730247563901155, test accuracy: 0.695652186870575
AUC ROC: 0.6560714285714285
Kappa: 0.25116279069767444
[[65 15]
[20 15]]
## ARTE FRANCÉS y SPECIALIZED
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.297583 1.225637 -0.367641 0.606499 0.072373
1 0.637676 -1.507256 -1.572737 -0.954161 -0.857425
2 2.236730 -0.319414 0.669910 -1.918119 -0.820882
3 0.662077 -0.381499 0.111981 -1.743808 -1.317593
4 0.736502 0.112932 -0.065024 -1.049458 -0.408043
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 -2.029620 0.791469 0.752018 2.268802 -1.383289
1 0.327005 0.816764 0.214245 0.241703 0.637066
2 -2.379333 -1.570021 -2.755344 -2.150610 -2.528577
3 -1.348534 -0.627198 -1.629882 -2.075974 -1.248765
4 -0.437499 0.090831 -0.852983 -1.922491 -0.284365
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 0.548279 1.903211 -1.011470
1 1.601538 0.300317 -0.466779
2 -0.877081 -0.522248 -1.429911
3 -1.126014 -1.316359 -1.126174
4 0.210624 -0.032122 -0.700183
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.006, 'max_iter': 2000}, que permiten obtener un Accuracy de 68.25% y un Kappa del 27.72
Model: "model_14"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_15 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_41 (Dense) (None, 30) 420
_________________________________________________________________
dense_42 (Dense) (None, 1) 31
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
134/134 [==============================] - 0s 52us/step
test loss: 0.643175168713527, test accuracy: 0.6567164063453674
AUC ROC: 0.6184593023255814
Kappa: 0.16067538126361647
[[75 11]
[35 13]]
## ARTE FRANCÉS y URBAN PLACE
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.297583 1.225637 -0.367641 0.606499 0.072373
1 0.637676 -1.507256 -1.572737 -0.954161 -0.857425
2 2.236730 -0.319414 0.669910 -1.918119 -0.820882
3 0.662077 -0.381499 0.111981 -1.743808 -1.317593
4 0.736502 0.112932 -0.065024 -1.049458 -0.408043
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 -2.029620 0.791469 0.752018 2.268802 -1.383289
1 0.327005 0.816764 0.214245 0.241703 0.637066
2 -2.379333 -1.570021 -2.755344 -2.150610 -2.528577
3 -1.348534 -0.627198 -1.629882 -2.075974 -1.248765
4 -0.437499 0.090831 -0.852983 -1.922491 -0.284365
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 0.548279 1.903211 -1.011470
1 1.601538 0.300317 -0.466779
2 -0.877081 -0.522248 -1.429911
3 -1.126014 -1.316359 -1.126174
4 0.210624 -0.032122 -0.700183
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-253-edca9a64a2bf> in <module> 28 grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True) 29 ---> 30 grid.fit(X_train, y_train) 31 32 print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format( C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params) 638 error_score=self.error_score) 639 for parameters, (train, test) in product(candidate_params, --> 640 cv.split(X, y, groups))) 641 642 # if one choose to see train score, "out" will contain train score info C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable) 787 # consumption. 788 self._iterating = False --> 789 self.retrieve() 790 # Make sure that we get a last message telling us we are done 791 elapsed_time = time.time() - self._start_time C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in retrieve(self) 697 try: 698 if getattr(self._backend, 'supports_timeout', False): --> 699 self._output.extend(job.get(timeout=self.timeout)) 700 else: 701 self._output.extend(job.get()) C:\ProgramData\Anaconda3\lib\multiprocessing\pool.py in get(self, timeout) 649 650 def get(self, timeout=None): --> 651 self.wait(timeout) 652 if not self.ready(): 653 raise TimeoutError C:\ProgramData\Anaconda3\lib\multiprocessing\pool.py in wait(self, timeout) 646 647 def wait(self, timeout=None): --> 648 self._event.wait(timeout) 649 650 def get(self, timeout=None): C:\ProgramData\Anaconda3\lib\threading.py in wait(self, timeout) 550 signaled = self._flag 551 if not signaled: --> 552 signaled = self._cond.wait(timeout) 553 return signaled 554 C:\ProgramData\Anaconda3\lib\threading.py in wait(self, timeout) 294 try: # restore state no matter what (e.g., KeyboardInterrupt) 295 if timeout is None: --> 296 waiter.acquire() 297 gotit = True 298 else: KeyboardInterrupt:
In [391]:
from IPython.display import display, Markdown, Latex
two_combs = list(itertools.combinations(range(len(companies)), 2))
for comb in two_combs[4:]:
display(Markdown('## '+companies[comb[0]]+' y '+companies[comb[1]]))
X = df_n_ps_std_mfcc[comb[0]].append(df_n_ps_std_mfcc[comb[1]])
y = df_n_ps[comb[0]]['chosen'].append(df_n_ps[comb[1]]['chosen'])
print(X.head())
X_train, X_test, y_train, y_test = train_test_split(X, y)
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
input_tensor = Input(shape = (n0,))
hidden_outputs = [input_tensor]
if grid.best_params_['activation'] == 'logistic':
grid.best_params_['activation'] = 'sigmoid'
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
print(model.summary())
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), verbose=0,
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=0
)
]
)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print("epochs: "+str(len(acc)))
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Arte Francés y Urban Place¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.297583 1.225637 -0.367641 0.606499 0.072373
1 0.637676 -1.507256 -1.572737 -0.954161 -0.857425
2 2.236730 -0.319414 0.669910 -1.918119 -0.820882
3 0.662077 -0.381499 0.111981 -1.743808 -1.317593
4 0.736502 0.112932 -0.065024 -1.049458 -0.408043
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 -2.029620 0.791469 0.752018 2.268802 -1.383289
1 0.327005 0.816764 0.214245 0.241703 0.637066
2 -2.379333 -1.570021 -2.755344 -2.150610 -2.528577
3 -1.348534 -0.627198 -1.629882 -2.075974 -1.248765
4 -0.437499 0.090831 -0.852983 -1.922491 -0.284365
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 0.548279 1.903211 -1.011470
1 1.601538 0.300317 -0.466779
2 -0.877081 -0.522248 -1.429911
3 -1.126014 -1.316359 -1.126174
4 0.210624 -0.032122 -0.700183
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.009, 'max_iter': 2000}, que permiten obtener un Accuracy de 74.12% y un Kappa del 33.40
Model: "model_20"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_22 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_59 (Dense) (None, 30) 420
_________________________________________________________________
dense_60 (Dense) (None, 1) 31
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
133/133 [==============================] - 0s 308us/step
test loss: 0.7585942951360143, test accuracy: 0.7067669034004211
AUC ROC: 0.6449612403100775
Kappa: 0.2724084724365269
[[77 13]
[26 17]]
Club De Banqueros y Empresarios y Gramma¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 -0.339415 0.847773 0.497198 -0.389310 1.225458
1 0.587658 -1.195426 0.636375 0.199876 0.765321
2 1.465595 -2.307943 0.354567 -0.058273 -1.298853
3 0.749403 -1.690498 -0.125200 -1.016135 0.825845
4 -0.280577 0.393332 0.744917 2.411400 -0.777421
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 1.947033 -0.736267 0.492219 0.576682 1.504697
1 0.061181 0.379367 -0.440867 0.232893 1.339920
2 -0.811453 -1.551580 -3.934320 -1.079432 2.546130
3 0.271444 -0.104786 -0.992141 0.049182 1.425948
4 -0.420018 1.258355 -1.544565 -0.498071 0.421527
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -1.796460 0.724954 0.958600
1 0.110001 0.807525 0.815678
2 1.421407 0.639359 0.199094
3 -0.343269 -0.789558 -0.411898
4 -0.632908 -0.056846 -0.072348
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (20, 10), 'learning_rate_init': 0.004, 'max_iter': 2000}, que permiten obtener un Accuracy de 78.47% y un Kappa del 42.89
Model: "model_21"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_23 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_61 (Dense) (None, 20) 280
_________________________________________________________________
dense_62 (Dense) (None, 10) 210
_________________________________________________________________
dense_63 (Dense) (None, 1) 11
=================================================================
Total params: 501
Trainable params: 501
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
118/118 [==============================] - 0s 42us/step
test loss: 0.5979639458454261, test accuracy: 0.7203390002250671
AUC ROC: 0.7169750081142485
Kappa: 0.3194687172317372
[[68 11]
[22 17]]
Club De Banqueros y Empresarios y Hotel Marrakech¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 -0.339415 0.847773 0.497198 -0.389310 1.225458
1 0.587658 -1.195426 0.636375 0.199876 0.765321
2 1.465595 -2.307943 0.354567 -0.058273 -1.298853
3 0.749403 -1.690498 -0.125200 -1.016135 0.825845
4 -0.280577 0.393332 0.744917 2.411400 -0.777421
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 1.947033 -0.736267 0.492219 0.576682 1.504697
1 0.061181 0.379367 -0.440867 0.232893 1.339920
2 -0.811453 -1.551580 -3.934320 -1.079432 2.546130
3 0.271444 -0.104786 -0.992141 0.049182 1.425948
4 -0.420018 1.258355 -1.544565 -0.498071 0.421527
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -1.796460 0.724954 0.958600
1 0.110001 0.807525 0.815678
2 1.421407 0.639359 0.199094
3 -0.343269 -0.789558 -0.411898
4 -0.632908 -0.056846 -0.072348
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (20, 20), 'learning_rate_init': 0.01, 'max_iter': 75}, que permiten obtener un Accuracy de 76.92% y un Kappa del 48.20
Model: "model_22"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_24 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_64 (Dense) (None, 20) 280
_________________________________________________________________
dense_65 (Dense) (None, 20) 420
_________________________________________________________________
dense_66 (Dense) (None, 1) 21
=================================================================
Total params: 721
Trainable params: 721
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
100/100 [==============================] - 0s 50us/step
test loss: 0.8634896659851075, test accuracy: 0.7200000286102295
AUC ROC: 0.78
Kappa: 0.35779816513761475
[[54 16]
[12 18]]
Club De Banqueros y Empresarios y Specialized¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 -0.339415 0.847773 0.497198 -0.389310 1.225458
1 0.587658 -1.195426 0.636375 0.199876 0.765321
2 1.465595 -2.307943 0.354567 -0.058273 -1.298853
3 0.749403 -1.690498 -0.125200 -1.016135 0.825845
4 -0.280577 0.393332 0.744917 2.411400 -0.777421
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 1.947033 -0.736267 0.492219 0.576682 1.504697
1 0.061181 0.379367 -0.440867 0.232893 1.339920
2 -0.811453 -1.551580 -3.934320 -1.079432 2.546130
3 0.271444 -0.104786 -0.992141 0.049182 1.425948
4 -0.420018 1.258355 -1.544565 -0.498071 0.421527
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -1.796460 0.724954 0.958600
1 0.110001 0.807525 0.815678
2 1.421407 0.639359 0.199094
3 -0.343269 -0.789558 -0.411898
4 -0.632908 -0.056846 -0.072348
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 30, 30), 'learning_rate_init': 0.002, 'max_iter': 100}, que permiten obtener un Accuracy de 74.37% y un Kappa del 45.55
Model: "model_23"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_25 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_67 (Dense) (None, 30) 420
_________________________________________________________________
dense_68 (Dense) (None, 30) 930
_________________________________________________________________
dense_69 (Dense) (None, 30) 930
_________________________________________________________________
dense_70 (Dense) (None, 1) 31
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
119/119 [==============================] - 0s 50us/step
test loss: 0.6695057942586786, test accuracy: 0.6974790096282959
AUC ROC: 0.7086538461538461
Kappa: 0.30431958428061057
[[63 17]
[19 20]]
Club De Banqueros y Empresarios y Urban Place¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 -0.339415 0.847773 0.497198 -0.389310 1.225458
1 0.587658 -1.195426 0.636375 0.199876 0.765321
2 1.465595 -2.307943 0.354567 -0.058273 -1.298853
3 0.749403 -1.690498 -0.125200 -1.016135 0.825845
4 -0.280577 0.393332 0.744917 2.411400 -0.777421
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 1.947033 -0.736267 0.492219 0.576682 1.504697
1 0.061181 0.379367 -0.440867 0.232893 1.339920
2 -0.811453 -1.551580 -3.934320 -1.079432 2.546130
3 0.271444 -0.104786 -0.992141 0.049182 1.425948
4 -0.420018 1.258355 -1.544565 -0.498071 0.421527
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -1.796460 0.724954 0.958600
1 0.110001 0.807525 0.815678
2 1.421407 0.639359 0.199094
3 -0.343269 -0.789558 -0.411898
4 -0.632908 -0.056846 -0.072348
Los parámetros del mejor modelo fueron {'activation': 'logistic', 'hidden_layer_sizes': (30, 30), 'learning_rate_init': 0.02, 'max_iter': 50}, que permiten obtener un Accuracy de 72.24% y un Kappa del 11.68
Model: "model_24"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_26 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_71 (Dense) (None, 30) 420
_________________________________________________________________
dense_72 (Dense) (None, 30) 930
_________________________________________________________________
dense_73 (Dense) (None, 1) 31
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
118/118 [==============================] - 0s 34us/step
test loss: 0.5854327123043901, test accuracy: 0.7033898234367371
AUC ROC: 0.6773026315789473
Kappa: 0.1924129839655847
[[74 6]
[29 9]]
Gramma y Hotel Marrakech¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 -0.674917 0.169246 0.673543 1.157142 -0.633186
1 0.277269 0.514176 0.200398 0.988939 -1.756594
2 1.483921 0.724793 0.473099 0.439577 -0.358096
3 -0.734008 -0.683844 -0.764866 -0.225060 -0.261235
4 -0.834815 -0.735908 -1.177596 -0.093532 0.508050
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 0.688145 0.215883 -0.452048 1.101066 0.064017
1 -0.022788 -0.235704 0.523508 -0.604231 1.188209
2 -0.452581 -0.213173 -0.596057 -0.767473 0.696227
3 -0.243429 0.588768 0.874148 1.302526 0.091256
4 0.503458 1.380798 1.847226 1.227896 0.017729
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -0.153703 1.751289 0.812723
1 0.863617 -0.801768 0.229305
2 -0.111259 -0.370649 -1.325817
3 -0.600323 -0.827452 0.390838
4 -0.329325 -0.953249 -0.125917
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 20, 10), 'learning_rate_init': 0.009, 'max_iter': 300}, que permiten obtener un Accuracy de 73.33% y un Kappa del 36.99
Model: "model_25"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_27 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_74 (Dense) (None, 30) 420
_________________________________________________________________
dense_75 (Dense) (None, 20) 620
_________________________________________________________________
dense_76 (Dense) (None, 10) 210
_________________________________________________________________
dense_77 (Dense) (None, 1) 11
=================================================================
Total params: 1,261
Trainable params: 1,261
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
90/90 [==============================] - 0s 33us/step
test loss: 2.603563933240043, test accuracy: 0.6555555462837219
AUC ROC: 0.6552779194288629
Kappa: 0.2552055525894287
[[43 10]
[21 16]]
Gramma y Specialized¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 -0.674917 0.169246 0.673543 1.157142 -0.633186
1 0.277269 0.514176 0.200398 0.988939 -1.756594
2 1.483921 0.724793 0.473099 0.439577 -0.358096
3 -0.734008 -0.683844 -0.764866 -0.225060 -0.261235
4 -0.834815 -0.735908 -1.177596 -0.093532 0.508050
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 0.688145 0.215883 -0.452048 1.101066 0.064017
1 -0.022788 -0.235704 0.523508 -0.604231 1.188209
2 -0.452581 -0.213173 -0.596057 -0.767473 0.696227
3 -0.243429 0.588768 0.874148 1.302526 0.091256
4 0.503458 1.380798 1.847226 1.227896 0.017729
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -0.153703 1.751289 0.812723
1 0.863617 -0.801768 0.229305
2 -0.111259 -0.370649 -1.325817
3 -0.600323 -0.827452 0.390838
4 -0.329325 -0.953249 -0.125917
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.005, 'max_iter': 1000}, que permiten obtener un Accuracy de 72.39% y un Kappa del 41.65
Model: "model_26"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_28 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_78 (Dense) (None, 30) 420
_________________________________________________________________
dense_79 (Dense) (None, 1) 31
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 1000
109/109 [==============================] - 0s 37us/step
test loss: 0.734237842603561, test accuracy: 0.5596330165863037
AUC ROC: 0.5234265734265734
Kappa: 0.02823179791976227
[[49 16]
[32 12]]
Gramma y Urban Place¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 -0.674917 0.169246 0.673543 1.157142 -0.633186
1 0.277269 0.514176 0.200398 0.988939 -1.756594
2 1.483921 0.724793 0.473099 0.439577 -0.358096
3 -0.734008 -0.683844 -0.764866 -0.225060 -0.261235
4 -0.834815 -0.735908 -1.177596 -0.093532 0.508050
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 0.688145 0.215883 -0.452048 1.101066 0.064017
1 -0.022788 -0.235704 0.523508 -0.604231 1.188209
2 -0.452581 -0.213173 -0.596057 -0.767473 0.696227
3 -0.243429 0.588768 0.874148 1.302526 0.091256
4 0.503458 1.380798 1.847226 1.227896 0.017729
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -0.153703 1.751289 0.812723
1 0.863617 -0.801768 0.229305
2 -0.111259 -0.370649 -1.325817
3 -0.600323 -0.827452 0.390838
4 -0.329325 -0.953249 -0.125917
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (10, 10), 'learning_rate_init': 0.001, 'max_iter': 50}, que permiten obtener un Accuracy de 71.91% y un Kappa del 15.28
Model: "model_27"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_29 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_80 (Dense) (None, 10) 140
_________________________________________________________________
dense_81 (Dense) (None, 10) 110
_________________________________________________________________
dense_82 (Dense) (None, 1) 11
=================================================================
Total params: 261
Trainable params: 261
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
108/108 [==============================] - 0s 56us/step
test loss: 0.6653945070725901, test accuracy: 0.5925925970077515
AUC ROC: 0.5785714285714285
Kappa: -0.08990825688073412
[[63 7]
[37 1]]
Hotel Marrakech y Specialized¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.221235 1.617887 0.929874 -0.231486 -0.525862
1 0.836735 -0.529605 -1.268139 -0.791053 0.815880
2 -0.190995 1.202756 0.050028 -2.631154 3.701544
3 0.521202 1.354284 1.423683 -0.634173 0.934734
4 0.250234 1.586078 -1.791096 0.127156 1.573000
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 1.384826 0.709441 0.512679 -2.231286 -2.278872
1 -1.992230 -0.371430 -0.356669 1.323871 0.946394
2 -1.158173 0.439586 2.317548 -2.282526 -1.571775
3 0.214772 -0.349135 1.009101 -2.193012 -0.301254
4 0.288525 1.962471 1.500627 1.352853 -1.921935
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -0.728806 -2.187766 -1.206544
1 -1.085097 0.673490 -1.496313
2 -2.541951 -2.587380 -2.132445
3 -0.356046 -0.668937 -0.421263
4 0.705405 -0.230103 -0.803009
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30, 20, 10), 'learning_rate_init': 0.009, 'max_iter': 75}, que permiten obtener un Accuracy de 67.65% y un Kappa del 34.61
Model: "model_28"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_30 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_83 (Dense) (None, 30) 420
_________________________________________________________________
dense_84 (Dense) (None, 20) 620
_________________________________________________________________
dense_85 (Dense) (None, 10) 210
_________________________________________________________________
dense_86 (Dense) (None, 1) 11
=================================================================
Total params: 1,261
Trainable params: 1,261
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
91/91 [==============================] - 0s 33us/step
test loss: 1.3281121267067206, test accuracy: 0.6373626589775085
AUC ROC: 0.6967455621301775
Kappa: 0.247557003257329
[[38 14]
[19 20]]
Hotel Marrakech y Urban Place¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.221235 1.617887 0.929874 -0.231486 -0.525862
1 0.836735 -0.529605 -1.268139 -0.791053 0.815880
2 -0.190995 1.202756 0.050028 -2.631154 3.701544
3 0.521202 1.354284 1.423683 -0.634173 0.934734
4 0.250234 1.586078 -1.791096 0.127156 1.573000
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 1.384826 0.709441 0.512679 -2.231286 -2.278872
1 -1.992230 -0.371430 -0.356669 1.323871 0.946394
2 -1.158173 0.439586 2.317548 -2.282526 -1.571775
3 0.214772 -0.349135 1.009101 -2.193012 -0.301254
4 0.288525 1.962471 1.500627 1.352853 -1.921935
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -0.728806 -2.187766 -1.206544
1 -1.085097 0.673490 -1.496313
2 -2.541951 -2.587380 -2.132445
3 -0.356046 -0.668937 -0.421263
4 0.705405 -0.230103 -0.803009
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 30, 30), 'learning_rate_init': 0.003, 'max_iter': 2000}, que permiten obtener un Accuracy de 71.48% y un Kappa del 37.61
Model: "model_29"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_31 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_87 (Dense) (None, 30) 420
_________________________________________________________________
dense_88 (Dense) (None, 30) 930
_________________________________________________________________
dense_89 (Dense) (None, 30) 930
_________________________________________________________________
dense_90 (Dense) (None, 1) 31
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
90/90 [==============================] - 0s 33us/step
test loss: 1.0477391746309068, test accuracy: 0.644444465637207
AUC ROC: 0.6894444444444443
Kappa: 0.1428571428571429
[[48 12]
[20 10]]
Specialized y Urban Place¶
mfccfiles_1 mfccfiles_2 mfccfiles_3 mfccfiles_4 mfccfiles_5 \
0 0.992062 -0.477172 -1.079451 -2.369470 -1.705431
1 0.843575 -0.507672 -0.731713 -0.334904 1.442336
2 0.816922 -0.263544 0.639646 -0.865417 1.276602
3 4.368525 0.851784 -0.671158 -0.128467 2.141169
4 0.001312 0.535305 -0.648296 0.221414 0.549478
mfccfiles_6 mfccfiles_7 mfccfiles_8 mfccfiles_9 mfccfiles_10 \
0 -0.098594 -0.281836 -1.432001 -0.898623 0.130446
1 -0.491141 -0.266416 -0.511246 1.004414 0.558777
2 -0.245238 0.106722 -0.761365 -0.170481 -1.443667
3 -0.472725 -1.437233 -1.858760 1.581800 -0.145852
4 0.736878 -0.439538 -0.138787 0.584258 0.095671
mfccfiles_11 mfccfiles_12 mfccfiles_13
0 -0.024683 -0.312128 0.020392
1 0.127114 -1.667555 0.835458
2 -0.451102 1.196430 -0.037846
3 0.107228 1.458238 1.666081
4 1.901833 2.909252 1.802578
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30, 30, 30), 'learning_rate_init': 0.01, 'max_iter': 50}, que permiten obtener un Accuracy de 68.10% y un Kappa del 33.67
Model: "model_30"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_32 (InputLayer) (None, 13) 0
_________________________________________________________________
dense_91 (Dense) (None, 30) 420
_________________________________________________________________
dense_92 (Dense) (None, 30) 930
_________________________________________________________________
dense_93 (Dense) (None, 30) 930
_________________________________________________________________
dense_94 (Dense) (None, 1) 31
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
109/109 [==============================] - 0s 37us/step
test loss: 1.1675174198019396, test accuracy: 0.6880733966827393
AUC ROC: 0.743271221532091
Kappa: 0.3372675250357654
[[52 11]
[23 23]]
Tonal Centroid¶
In [131]:
df_n_ps_std[0].columns
Out[131]:
Index(['durationfiles', 'rmsfiles', 'rmsmedianfiles', 'lowenergyfiles',
'ASRfiles', 'beatspectrumfiles', 'eventdensityfiles', 'tempofiles',
'pulseclarityfiles', 'zerocrossfiles', 'rolloffsfiles',
'brightnessfiles', 'spreadfiles', 'centroidfiles', 'kurtosisfiles',
'flatnessfiles', 'entropyfiles', 'mfccfiles_1', 'mfccfiles_2',
'mfccfiles_3', 'mfccfiles_4', 'mfccfiles_5', 'mfccfiles_6',
'mfccfiles_7', 'mfccfiles_8', 'mfccfiles_9', 'mfccfiles_10',
'mfccfiles_11', 'mfccfiles_12', 'mfccfiles_13', 'inharmonicityfiles',
'bestkeyfiles', 'keyclarityfiles', 'modalityfiles',
'tonalcentroidfiles_1', 'tonalcentroidfiles_2', 'tonalcentroidfiles_3',
'tonalcentroidfiles_4', 'tonalcentroidfiles_5', 'tonalcentroidfiles_6',
'chromagramfiles_1', 'chromagramfiles_2', 'chromagramfiles_3',
'chromagramfiles_4', 'chromagramfiles_5', 'chromagramfiles_6',
'chromagramfiles_7', 'chromagramfiles_8', 'chromagramfiles_9',
'chromagramfiles_10', 'chromagramfiles_11', 'chromagramfiles_12',
'attackslopefiles', 'attackleapfiles', 'chosen'],
dtype='object')
In [132]:
df_n_ps_std[0].columns[34:40]
Out[132]:
Index(['tonalcentroidfiles_1', 'tonalcentroidfiles_2', 'tonalcentroidfiles_3',
'tonalcentroidfiles_4', 'tonalcentroidfiles_5', 'tonalcentroidfiles_6'],
dtype='object')
In [133]:
df_n_ps_std_tc = [None]*len(companies)
for i in range(len(companies)):
df_n_ps_std_tc[i] = pd.DataFrame(df_n_ps_std[i].iloc[:,34:40])
df_n_ps_std_tc[i].columns=df_n_ps_std[i].columns[34:40]
df_n_ps_std_tc[0].info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 315 entries, 0 to 314 Data columns (total 6 columns): tonalcentroidfiles_1 315 non-null float64 tonalcentroidfiles_2 315 non-null float64 tonalcentroidfiles_3 315 non-null float64 tonalcentroidfiles_4 315 non-null float64 tonalcentroidfiles_5 315 non-null float64 tonalcentroidfiles_6 315 non-null float64 dtypes: float64(6) memory usage: 14.8 KB
Arte Francés¶
ANN¶
In [134]:
X = df_n_ps_std_tc[0]
In [135]:
y = df_n_ps[0]['chosen']
In [136]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [137]:
X_train.shape
Out[137]:
(236, 6)
In [138]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [139]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [140]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [226]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (10, 10, 10), 'learning_rate_init': 0.001, 'max_iter': 100}, que permiten obtener un Accuracy de 76.27% y un Kappa del 13.18
Tiempo total: 23.63 minutos
In [141]:
grid.best_params_= {'activation': 'tanh', 'hidden_layer_sizes': (10, 10, 10), 'learning_rate_init': 0.001, 'max_iter': 100}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [142]:
input_tensor = Input(shape = (n0,))
In [143]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [144]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [145]:
model.summary()
Model: "model_7" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_8 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_20 (Dense) (None, 10) 70 _________________________________________________________________ dense_21 (Dense) (None, 10) 110 _________________________________________________________________ dense_22 (Dense) (None, 10) 110 _________________________________________________________________ dense_23 (Dense) (None, 1) 11 ================================================================= Total params: 301 Trainable params: 301 Non-trainable params: 0 _________________________________________________________________
In [146]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 236 samples, validate on 79 samples Epoch 1/100 236/236 [==============================] - 0s 970us/step - loss: 0.7070 - accuracy: 0.4915 - val_loss: 0.7205 - val_accuracy: 0.4430 Epoch 2/100 236/236 [==============================] - 0s 80us/step - loss: 0.6903 - accuracy: 0.5127 - val_loss: 0.7068 - val_accuracy: 0.4810 Epoch 3/100 236/236 [==============================] - 0s 68us/step - loss: 0.6757 - accuracy: 0.5508 - val_loss: 0.6955 - val_accuracy: 0.4810 Epoch 4/100 236/236 [==============================] - 0s 97us/step - loss: 0.6638 - accuracy: 0.5975 - val_loss: 0.6854 - val_accuracy: 0.5570 Epoch 5/100 236/236 [==============================] - 0s 72us/step - loss: 0.6517 - accuracy: 0.6271 - val_loss: 0.6767 - val_accuracy: 0.6076 Epoch 6/100 236/236 [==============================] - 0s 68us/step - loss: 0.6413 - accuracy: 0.6737 - val_loss: 0.6680 - val_accuracy: 0.6456 Epoch 7/100 236/236 [==============================] - 0s 68us/step - loss: 0.6329 - accuracy: 0.7246 - val_loss: 0.6604 - val_accuracy: 0.6962 Epoch 8/100 236/236 [==============================] - 0s 72us/step - loss: 0.6245 - accuracy: 0.7246 - val_loss: 0.6541 - val_accuracy: 0.7089 Epoch 9/100 236/236 [==============================] - 0s 110us/step - loss: 0.6168 - accuracy: 0.7288 - val_loss: 0.6486 - val_accuracy: 0.7089 Epoch 10/100 236/236 [==============================] - 0s 93us/step - loss: 0.6098 - accuracy: 0.7288 - val_loss: 0.6442 - val_accuracy: 0.7089 Epoch 11/100 236/236 [==============================] - 0s 89us/step - loss: 0.6043 - accuracy: 0.7288 - val_loss: 0.6406 - val_accuracy: 0.7089 Epoch 12/100 236/236 [==============================] - 0s 89us/step - loss: 0.5992 - accuracy: 0.7288 - val_loss: 0.6369 - val_accuracy: 0.7089 Epoch 13/100 236/236 [==============================] - 0s 97us/step - loss: 0.5948 - accuracy: 0.7288 - val_loss: 0.6344 - val_accuracy: 0.7089 Epoch 14/100 236/236 [==============================] - 0s 85us/step - loss: 0.5915 - accuracy: 0.7288 - val_loss: 0.6306 - val_accuracy: 0.7089 Epoch 15/100 236/236 [==============================] - 0s 102us/step - loss: 0.5877 - accuracy: 0.7288 - val_loss: 0.6292 - val_accuracy: 0.7089 Epoch 16/100 236/236 [==============================] - 0s 93us/step - loss: 0.5862 - accuracy: 0.7288 - val_loss: 0.6263 - val_accuracy: 0.7089 Epoch 17/100 236/236 [==============================] - 0s 106us/step - loss: 0.5835 - accuracy: 0.7288 - val_loss: 0.6253 - val_accuracy: 0.7089 Epoch 18/100 236/236 [==============================] - 0s 97us/step - loss: 0.5818 - accuracy: 0.7288 - val_loss: 0.6246 - val_accuracy: 0.7089 Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 19/100 236/236 [==============================] - 0s 93us/step - loss: 0.5804 - accuracy: 0.7288 - val_loss: 0.6240 - val_accuracy: 0.7089 Epoch 20/100 236/236 [==============================] - 0s 97us/step - loss: 0.5800 - accuracy: 0.7288 - val_loss: 0.6227 - val_accuracy: 0.7089 Epoch 21/100 236/236 [==============================] - 0s 89us/step - loss: 0.5795 - accuracy: 0.7288 - val_loss: 0.6221 - val_accuracy: 0.7089 Epoch 22/100 236/236 [==============================] - 0s 89us/step - loss: 0.5789 - accuracy: 0.7288 - val_loss: 0.6217 - val_accuracy: 0.7089 Epoch 23/100 236/236 [==============================] - 0s 93us/step - loss: 0.5784 - accuracy: 0.7288 - val_loss: 0.6214 - val_accuracy: 0.7089 Epoch 24/100 236/236 [==============================] - 0s 93us/step - loss: 0.5778 - accuracy: 0.7288 - val_loss: 0.6212 - val_accuracy: 0.7089 Epoch 25/100 236/236 [==============================] - 0s 93us/step - loss: 0.5776 - accuracy: 0.7288 - val_loss: 0.6207 - val_accuracy: 0.7089 Epoch 26/100 236/236 [==============================] - 0s 85us/step - loss: 0.5771 - accuracy: 0.7288 - val_loss: 0.6205 - val_accuracy: 0.7089 Epoch 27/100 236/236 [==============================] - 0s 97us/step - loss: 0.5768 - accuracy: 0.7288 - val_loss: 0.6204 - val_accuracy: 0.7089 Epoch 28/100 236/236 [==============================] - 0s 89us/step - loss: 0.5765 - accuracy: 0.7288 - val_loss: 0.6193 - val_accuracy: 0.7089 Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 29/100 236/236 [==============================] - 0s 93us/step - loss: 0.5762 - accuracy: 0.7288 - val_loss: 0.6190 - val_accuracy: 0.7089 Epoch 30/100 236/236 [==============================] - 0s 93us/step - loss: 0.5759 - accuracy: 0.7288 - val_loss: 0.6188 - val_accuracy: 0.7089 Epoch 31/100 236/236 [==============================] - 0s 89us/step - loss: 0.5757 - accuracy: 0.7288 - val_loss: 0.6186 - val_accuracy: 0.7089 Epoch 32/100 236/236 [==============================] - 0s 93us/step - loss: 0.5755 - accuracy: 0.7288 - val_loss: 0.6184 - val_accuracy: 0.7089 Epoch 33/100 236/236 [==============================] - 0s 102us/step - loss: 0.5753 - accuracy: 0.7288 - val_loss: 0.6182 - val_accuracy: 0.7089 Epoch 34/100 236/236 [==============================] - 0s 119us/step - loss: 0.5751 - accuracy: 0.7288 - val_loss: 0.6180 - val_accuracy: 0.7089 Epoch 35/100 236/236 [==============================] - 0s 102us/step - loss: 0.5750 - accuracy: 0.7288 - val_loss: 0.6178 - val_accuracy: 0.7089 Epoch 36/100 236/236 [==============================] - 0s 102us/step - loss: 0.5749 - accuracy: 0.7288 - val_loss: 0.6175 - val_accuracy: 0.7089 Epoch 37/100 236/236 [==============================] - 0s 93us/step - loss: 0.5746 - accuracy: 0.7288 - val_loss: 0.6176 - val_accuracy: 0.7089 Epoch 38/100 236/236 [==============================] - 0s 93us/step - loss: 0.5744 - accuracy: 0.7288 - val_loss: 0.6175 - val_accuracy: 0.7089 Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 39/100 236/236 [==============================] - 0s 102us/step - loss: 0.5743 - accuracy: 0.7288 - val_loss: 0.6175 - val_accuracy: 0.7089 Epoch 40/100 236/236 [==============================] - 0s 93us/step - loss: 0.5742 - accuracy: 0.7288 - val_loss: 0.6175 - val_accuracy: 0.7089 Epoch 41/100 236/236 [==============================] - 0s 97us/step - loss: 0.5742 - accuracy: 0.7288 - val_loss: 0.6173 - val_accuracy: 0.7089 Epoch 42/100 236/236 [==============================] - 0s 93us/step - loss: 0.5740 - accuracy: 0.7288 - val_loss: 0.6173 - val_accuracy: 0.7089 Epoch 43/100 236/236 [==============================] - 0s 114us/step - loss: 0.5740 - accuracy: 0.7288 - val_loss: 0.6172 - val_accuracy: 0.7089 Epoch 44/100 236/236 [==============================] - 0s 97us/step - loss: 0.5739 - accuracy: 0.7288 - val_loss: 0.6171 - val_accuracy: 0.7089 Epoch 45/100 236/236 [==============================] - 0s 93us/step - loss: 0.5738 - accuracy: 0.7288 - val_loss: 0.6171 - val_accuracy: 0.7089 Epoch 46/100 236/236 [==============================] - 0s 97us/step - loss: 0.5738 - accuracy: 0.7288 - val_loss: 0.6170 - val_accuracy: 0.7089 Epoch 47/100 236/236 [==============================] - 0s 93us/step - loss: 0.5737 - accuracy: 0.7288 - val_loss: 0.6171 - val_accuracy: 0.7089 Epoch 48/100 236/236 [==============================] - 0s 123us/step - loss: 0.5735 - accuracy: 0.7288 - val_loss: 0.6170 - val_accuracy: 0.7089 Epoch 00048: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 49/100 236/236 [==============================] - 0s 93us/step - loss: 0.5735 - accuracy: 0.7288 - val_loss: 0.6170 - val_accuracy: 0.7089 Epoch 50/100 236/236 [==============================] - 0s 93us/step - loss: 0.5734 - accuracy: 0.7288 - val_loss: 0.6170 - val_accuracy: 0.7089 Epoch 51/100 236/236 [==============================] - 0s 89us/step - loss: 0.5734 - accuracy: 0.7288 - val_loss: 0.6170 - val_accuracy: 0.7089 Epoch 52/100 236/236 [==============================] - 0s 89us/step - loss: 0.5733 - accuracy: 0.7288 - val_loss: 0.6169 - val_accuracy: 0.7089 Epoch 53/100 236/236 [==============================] - 0s 89us/step - loss: 0.5733 - accuracy: 0.7288 - val_loss: 0.6169 - val_accuracy: 0.7089 Epoch 54/100 236/236 [==============================] - 0s 97us/step - loss: 0.5733 - accuracy: 0.7288 - val_loss: 0.6169 - val_accuracy: 0.7089 Epoch 55/100 236/236 [==============================] - 0s 89us/step - loss: 0.5732 - accuracy: 0.7288 - val_loss: 0.6169 - val_accuracy: 0.7089 Epoch 56/100 236/236 [==============================] - 0s 102us/step - loss: 0.5732 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 57/100 236/236 [==============================] - 0s 89us/step - loss: 0.5731 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 58/100 236/236 [==============================] - 0s 97us/step - loss: 0.5731 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 00058: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 59/100 236/236 [==============================] - 0s 89us/step - loss: 0.5731 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 60/100 236/236 [==============================] - 0s 97us/step - loss: 0.5730 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 61/100 236/236 [==============================] - 0s 89us/step - loss: 0.5730 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 62/100 236/236 [==============================] - 0s 93us/step - loss: 0.5730 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 63/100 236/236 [==============================] - 0s 110us/step - loss: 0.5730 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 64/100 236/236 [==============================] - 0s 131us/step - loss: 0.5730 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 65/100 236/236 [==============================] - 0s 102us/step - loss: 0.5729 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 66/100 236/236 [==============================] - 0s 102us/step - loss: 0.5729 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 67/100 236/236 [==============================] - 0s 110us/step - loss: 0.5729 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 68/100 236/236 [==============================] - 0s 102us/step - loss: 0.5729 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 00068: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 69/100 236/236 [==============================] - 0s 106us/step - loss: 0.5729 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 70/100 236/236 [==============================] - 0s 97us/step - loss: 0.5729 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 71/100 236/236 [==============================] - 0s 97us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 72/100 236/236 [==============================] - 0s 102us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 73/100 236/236 [==============================] - 0s 97us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 74/100 236/236 [==============================] - 0s 97us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 75/100 236/236 [==============================] - 0s 110us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 76/100 236/236 [==============================] - 0s 97us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 77/100 236/236 [==============================] - 0s 102us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 78/100 236/236 [==============================] - 0s 114us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6168 - val_accuracy: 0.7089 Epoch 00078: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 79/100 236/236 [==============================] - 0s 93us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 80/100 236/236 [==============================] - 0s 119us/step - loss: 0.5728 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 81/100 236/236 [==============================] - 0s 119us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 82/100 236/236 [==============================] - 0s 110us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 83/100 236/236 [==============================] - 0s 93us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 84/100 236/236 [==============================] - 0s 102us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 85/100 236/236 [==============================] - 0s 123us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 86/100 236/236 [==============================] - 0s 102us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 87/100 236/236 [==============================] - 0s 102us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 88/100 236/236 [==============================] - 0s 106us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 00088: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 89/100 236/236 [==============================] - 0s 114us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 90/100 236/236 [==============================] - 0s 110us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 91/100 236/236 [==============================] - 0s 102us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 92/100 236/236 [==============================] - 0s 110us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 93/100 236/236 [==============================] - 0s 106us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 94/100 236/236 [==============================] - 0s 106us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 95/100 236/236 [==============================] - 0s 106us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 96/100 236/236 [==============================] - 0s 114us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 97/100 236/236 [==============================] - 0s 131us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 98/100 236/236 [==============================] - 0s 110us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 00098: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 99/100 236/236 [==============================] - 0s 110us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089 Epoch 100/100 236/236 [==============================] - 0s 106us/step - loss: 0.5727 - accuracy: 0.7288 - val_loss: 0.6167 - val_accuracy: 0.7089
In [147]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 100)
In [148]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
79/79 [==============================] - 0s 63us/step test loss: 0.6167174675796605, test accuracy: 0.7088607549667358
In [149]:
y_pred = model.predict(X_test)
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.0 AUC ROC: 0.5 [[56 0] [23 0]]
KMeans¶
In [236]:
X
Out[236]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 1.585484 | 0.923309 | -0.748807 | 1.209820 | -1.073924 | 0.283035 |
| 1 | 1.129768 | 0.963814 | 0.209096 | -0.143471 | 0.115184 | 0.020905 |
| 2 | -0.066076 | 1.857866 | 1.921193 | 1.093619 | 2.089353 | 1.984310 |
| 3 | 0.119831 | 1.429286 | 1.472808 | 1.282029 | 1.096897 | 2.122871 |
| 4 | -0.123292 | 0.197415 | 0.503797 | 1.431215 | 1.715761 | 0.683611 |
| 5 | -0.553148 | 0.127288 | 2.842797 | -1.267536 | 1.273635 | 2.271003 |
| 6 | -0.391161 | -0.277979 | 1.117190 | 0.713713 | -1.700239 | -0.587212 |
| 7 | -0.538913 | 1.251767 | 0.804764 | 0.214490 | -0.879193 | 0.306379 |
| 8 | -0.032875 | 0.470932 | -0.097587 | 0.648742 | -1.031819 | 0.164361 |
| 9 | 0.212612 | 0.417050 | 0.248906 | -1.495698 | 1.707020 | -0.592557 |
| 10 | 0.503861 | 0.931556 | -0.151041 | -0.292255 | -0.447486 | -0.196969 |
| 11 | 0.279193 | 0.515238 | 0.967780 | 0.662445 | 0.209303 | 0.507757 |
| 12 | 0.049966 | -0.352640 | -0.595831 | 0.468652 | -1.490626 | 0.103993 |
| 13 | 0.224941 | -0.251218 | 0.462542 | 0.909846 | -1.326305 | -0.026245 |
| 14 | -1.007562 | -0.018176 | -1.310793 | -1.236878 | 1.573941 | -1.709828 |
| 15 | -0.156222 | 0.809961 | 0.891274 | -0.560523 | 1.892119 | -0.767684 |
| 16 | -0.561577 | 0.649671 | 0.616195 | -1.560389 | 1.268177 | -1.094196 |
| 17 | 0.828743 | -1.472369 | 0.354955 | 0.144804 | -1.811789 | -0.965157 |
| 18 | 0.520383 | -1.982735 | -0.524125 | -0.227001 | -1.732849 | -1.131893 |
| 19 | 0.619988 | -1.486445 | 0.400141 | -0.931997 | -2.408325 | -0.039112 |
| 20 | -1.718572 | 1.328228 | -0.790084 | -2.422930 | -1.075452 | -0.362318 |
| 21 | -1.673788 | -0.008250 | -0.699254 | -0.669847 | 0.358439 | -1.911212 |
| 22 | -1.660892 | 0.201931 | -1.149618 | -0.154362 | -1.229344 | -1.403777 |
| 23 | 0.467761 | 1.771253 | -2.115824 | -2.524369 | -0.191251 | 0.283914 |
| 24 | 1.365177 | 1.009311 | 0.060605 | -0.430112 | -1.445585 | 0.147080 |
| 25 | 0.526073 | -0.108881 | -0.890367 | 0.453905 | -1.553338 | 1.635917 |
| 26 | 0.396699 | 1.270857 | -0.933936 | -0.265038 | -0.598090 | 1.282414 |
| 27 | 1.042904 | 1.615651 | -1.342135 | -1.108659 | -0.228062 | 0.704441 |
| 28 | 0.488482 | 1.667605 | -0.222726 | -1.289806 | 0.833486 | -0.007520 |
| 29 | -0.206765 | -0.088250 | 0.214115 | 0.280450 | -0.032652 | 1.281632 |
| ... | ... | ... | ... | ... | ... | ... |
| 285 | 0.761670 | -1.885823 | 0.301159 | -0.516737 | -0.384864 | -0.542683 |
| 286 | 2.233841 | -0.021303 | 1.621452 | -1.116993 | 0.705855 | -1.623585 |
| 287 | 0.933521 | 0.065790 | -1.295122 | 0.574358 | -0.278402 | 2.277615 |
| 288 | 1.398839 | -0.456314 | -1.182173 | 0.348139 | 0.231267 | 1.399962 |
| 289 | 0.982720 | 0.097900 | -0.814050 | 0.852544 | 0.308591 | 1.608777 |
| 290 | 0.959009 | -1.443293 | -0.329974 | -0.253115 | 0.724219 | -0.415649 |
| 291 | -3.186710 | 0.207715 | -1.442295 | -0.713479 | -0.644843 | 0.684665 |
| 292 | -2.266002 | 0.208427 | 0.090970 | 0.014667 | -0.927106 | 1.146918 |
| 293 | 1.473030 | 0.944250 | -0.160216 | 0.323871 | -0.664953 | 0.727193 |
| 294 | 2.116511 | 1.003706 | -1.374891 | -0.601957 | -1.760610 | 0.014541 |
| 295 | 1.516890 | 0.883674 | -1.850520 | 0.688076 | -1.350999 | 0.620360 |
| 296 | -0.171687 | 0.469515 | 0.407395 | 1.081823 | -1.053878 | -0.023872 |
| 297 | -0.023957 | 0.051075 | 0.045786 | 0.108234 | -0.643408 | 0.527902 |
| 298 | 0.152215 | 0.030843 | 0.217573 | 0.063538 | -0.471831 | 0.840207 |
| 299 | -0.286639 | 0.215830 | -0.245963 | 0.927776 | -0.474599 | 0.233343 |
| 300 | 0.411970 | 0.642559 | -0.319323 | 1.141506 | -0.291830 | 0.197165 |
| 301 | 0.915776 | 0.420715 | -0.435877 | 0.621147 | -0.746445 | 0.397040 |
| 302 | -0.963660 | -2.504276 | 0.149799 | 1.260867 | 0.289108 | -0.386784 |
| 303 | -0.732467 | -1.137228 | -0.806385 | 1.023830 | -0.646676 | -0.876828 |
| 304 | 0.175270 | -2.037232 | -1.136866 | -0.405235 | -0.294667 | 0.533651 |
| 305 | 1.507133 | -2.022655 | -0.176808 | -2.220532 | -0.028497 | 1.571387 |
| 306 | 3.112152 | -1.221959 | 0.020285 | -2.830754 | -0.654301 | 0.750704 |
| 307 | 2.013689 | -1.475971 | -1.690376 | -2.773483 | -0.813379 | 2.885407 |
| 308 | 1.511606 | 0.675622 | -0.743850 | -1.491272 | -0.807844 | -0.418377 |
| 309 | 1.412105 | 1.763606 | -0.378812 | -1.313665 | -0.185153 | -0.847850 |
| 310 | 1.122395 | 0.814854 | -0.854500 | -1.402116 | 0.114392 | -0.882142 |
| 311 | 0.336108 | 0.216775 | 0.080290 | -0.047519 | -0.756037 | -1.071728 |
| 312 | 1.201358 | 0.783381 | -0.221010 | 0.308862 | -1.206838 | -0.690837 |
| 313 | -0.672924 | -0.268212 | 1.143994 | -0.147440 | 2.008975 | -0.652644 |
| 314 | -0.034368 | 1.050289 | 0.424944 | 0.805168 | -0.376947 | -0.658873 |
315 rows × 6 columns
In [237]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[237]:
[1890.0, 1580.620167230212, 1363.1774930694505, 1195.4345875172114, 1096.6320586530273, 1013.9501053083941, 953.1588875587528, 882.9139429264524, 844.4886425171022, 815.6880457942883, 788.9746059037274, 745.3922927967658, 722.2827052057237, 709.9626886481943]
In [238]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[238]:
[<matplotlib.lines.Line2D at 0x1b829b146a0>]
K=4
In [239]:
kmeans_tc = KMeans(n_clusters=4, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[239]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [240]:
kmeans_tc.labels_
Out[240]:
array([1, 1, 2, 2, 2, 2, 3, 2, 2, 0, 1, 2, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0,
3, 0, 1, 1, 1, 1, 0, 2, 2, 0, 0, 3, 3, 3, 3, 3, 2, 2, 2, 0, 0, 0,
1, 1, 1, 1, 1, 3, 3, 2, 2, 2, 0, 0, 3, 0, 0, 2, 2, 2, 0, 0, 0, 0,
1, 1, 2, 2, 2, 1, 1, 1, 0, 2, 0, 2, 2, 2, 2, 0, 2, 0, 1, 0, 0, 0,
3, 1, 2, 3, 0, 3, 3, 1, 3, 2, 3, 3, 2, 2, 2, 0, 2, 2, 2, 2, 3, 3,
1, 2, 2, 2, 3, 0, 1, 3, 1, 2, 0, 3, 3, 3, 1, 3, 1, 1, 0, 0, 2, 2,
3, 3, 2, 1, 1, 1, 1, 1, 3, 1, 3, 3, 0, 2, 3, 1, 0, 1, 1, 0, 1, 2,
1, 1, 1, 2, 3, 2, 3, 1, 3, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 3, 1, 3,
1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 2, 1, 2, 1, 3, 1, 1, 0,
0, 0, 3, 1, 1, 0, 0, 0, 0, 1, 3, 3, 3, 0, 0, 3, 3, 3, 1, 1, 3, 0,
1, 2, 1, 1, 0, 0, 0, 0, 3, 2, 2, 1, 1, 1, 2, 0, 1, 0, 1, 3, 2, 2,
0, 2, 2, 2, 1, 2, 3, 3, 2, 2, 2, 0, 2, 2, 3, 3, 0, 3, 3, 0, 0, 1,
1, 3, 3, 3, 3, 3, 3, 1, 2, 2, 0, 2, 2, 2, 1, 1, 0, 1, 3, 3, 3, 3,
0, 1, 1, 1, 1, 0, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 1, 1, 1, 1,
1, 0, 0, 3, 1, 0, 2])
In [241]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[241]:
array([1, 1, 2, 2, 2, 2, 3, 2, 2, 0, 1, 2, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0,
3, 0, 1, 1, 1, 1, 0, 2, 2, 0, 0, 3, 3, 3, 3, 3, 2, 2, 2, 0, 0, 0,
1, 1, 1, 1, 1, 3, 3, 2, 2, 2, 0, 0, 3, 0, 0, 2, 2, 2, 0, 0, 0, 0,
1, 1, 2, 2, 2, 1, 1, 1, 0, 2, 0, 2, 2, 2, 2, 0, 2, 0, 1, 0, 0, 0,
3, 1, 2, 3, 0, 3, 3, 1, 3, 2, 3, 3, 2, 2, 2, 0, 2, 2, 2, 2, 3, 3,
1, 2, 2, 2, 3, 0, 1, 3, 1, 2, 0, 3, 3, 3, 1, 3, 1, 1, 0, 0, 2, 2,
3, 3, 2, 1, 1, 1, 1, 1, 3, 1, 3, 3, 0, 2, 3, 1, 0, 1, 1, 0, 1, 2,
1, 1, 1, 2, 3, 2, 3, 1, 3, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 3, 1, 3,
1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 2, 1, 2, 1, 3, 1, 1, 0,
0, 0, 3, 1, 1, 0, 0, 0, 0, 1, 3, 3, 3, 0, 0, 3, 3, 3, 1, 1, 3, 0,
1, 2, 1, 1, 0, 0, 0, 0, 3, 2, 2, 1, 1, 1, 2, 0, 1, 0, 1, 3, 2, 2,
0, 2, 2, 2, 1, 2, 3, 3, 2, 2, 2, 0, 2, 2, 3, 3, 0, 3, 3, 0, 0, 1,
1, 3, 3, 3, 3, 3, 3, 1, 2, 2, 0, 2, 2, 2, 1, 1, 0, 1, 3, 3, 3, 3,
0, 1, 1, 1, 1, 0, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 1, 1, 1, 1,
1, 0, 0, 3, 1, 0, 2])
In [242]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [243]:
X
Out[243]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1.585484 | 0.923309 | -0.748807 | 1.209820 | -1.073924 | 0.283035 | 1 | 0 |
| 1 | 1.129768 | 0.963814 | 0.209096 | -0.143471 | 0.115184 | 0.020905 | 1 | 0 |
| 2 | -0.066076 | 1.857866 | 1.921193 | 1.093619 | 2.089353 | 1.984310 | 2 | 0 |
| 3 | 0.119831 | 1.429286 | 1.472808 | 1.282029 | 1.096897 | 2.122871 | 2 | 0 |
| 4 | -0.123292 | 0.197415 | 0.503797 | 1.431215 | 1.715761 | 0.683611 | 2 | 0 |
| 5 | -0.553148 | 0.127288 | 2.842797 | -1.267536 | 1.273635 | 2.271003 | 2 | 0 |
| 6 | -0.391161 | -0.277979 | 1.117190 | 0.713713 | -1.700239 | -0.587212 | 3 | 0 |
| 7 | -0.538913 | 1.251767 | 0.804764 | 0.214490 | -0.879193 | 0.306379 | 2 | 0 |
| 8 | -0.032875 | 0.470932 | -0.097587 | 0.648742 | -1.031819 | 0.164361 | 2 | 0 |
| 9 | 0.212612 | 0.417050 | 0.248906 | -1.495698 | 1.707020 | -0.592557 | 0 | 0 |
| 10 | 0.503861 | 0.931556 | -0.151041 | -0.292255 | -0.447486 | -0.196969 | 1 | 0 |
| 11 | 0.279193 | 0.515238 | 0.967780 | 0.662445 | 0.209303 | 0.507757 | 2 | 0 |
| 12 | 0.049966 | -0.352640 | -0.595831 | 0.468652 | -1.490626 | 0.103993 | 3 | 0 |
| 13 | 0.224941 | -0.251218 | 0.462542 | 0.909846 | -1.326305 | -0.026245 | 3 | 0 |
| 14 | -1.007562 | -0.018176 | -1.310793 | -1.236878 | 1.573941 | -1.709828 | 0 | 0 |
| 15 | -0.156222 | 0.809961 | 0.891274 | -0.560523 | 1.892119 | -0.767684 | 0 | 0 |
| 16 | -0.561577 | 0.649671 | 0.616195 | -1.560389 | 1.268177 | -1.094196 | 0 | 0 |
| 17 | 0.828743 | -1.472369 | 0.354955 | 0.144804 | -1.811789 | -0.965157 | 3 | 0 |
| 18 | 0.520383 | -1.982735 | -0.524125 | -0.227001 | -1.732849 | -1.131893 | 3 | 0 |
| 19 | 0.619988 | -1.486445 | 0.400141 | -0.931997 | -2.408325 | -0.039112 | 3 | 0 |
| 20 | -1.718572 | 1.328228 | -0.790084 | -2.422930 | -1.075452 | -0.362318 | 0 | 0 |
| 21 | -1.673788 | -0.008250 | -0.699254 | -0.669847 | 0.358439 | -1.911212 | 0 | 0 |
| 22 | -1.660892 | 0.201931 | -1.149618 | -0.154362 | -1.229344 | -1.403777 | 3 | 0 |
| 23 | 0.467761 | 1.771253 | -2.115824 | -2.524369 | -0.191251 | 0.283914 | 0 | 0 |
| 24 | 1.365177 | 1.009311 | 0.060605 | -0.430112 | -1.445585 | 0.147080 | 1 | 0 |
| 25 | 0.526073 | -0.108881 | -0.890367 | 0.453905 | -1.553338 | 1.635917 | 1 | 0 |
| 26 | 0.396699 | 1.270857 | -0.933936 | -0.265038 | -0.598090 | 1.282414 | 1 | 0 |
| 27 | 1.042904 | 1.615651 | -1.342135 | -1.108659 | -0.228062 | 0.704441 | 1 | 0 |
| 28 | 0.488482 | 1.667605 | -0.222726 | -1.289806 | 0.833486 | -0.007520 | 0 | 0 |
| 29 | -0.206765 | -0.088250 | 0.214115 | 0.280450 | -0.032652 | 1.281632 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 285 | 0.761670 | -1.885823 | 0.301159 | -0.516737 | -0.384864 | -0.542683 | 3 | 1 |
| 286 | 2.233841 | -0.021303 | 1.621452 | -1.116993 | 0.705855 | -1.623585 | 0 | 1 |
| 287 | 0.933521 | 0.065790 | -1.295122 | 0.574358 | -0.278402 | 2.277615 | 1 | 1 |
| 288 | 1.398839 | -0.456314 | -1.182173 | 0.348139 | 0.231267 | 1.399962 | 1 | 1 |
| 289 | 0.982720 | 0.097900 | -0.814050 | 0.852544 | 0.308591 | 1.608777 | 1 | 1 |
| 290 | 0.959009 | -1.443293 | -0.329974 | -0.253115 | 0.724219 | -0.415649 | 1 | 1 |
| 291 | -3.186710 | 0.207715 | -1.442295 | -0.713479 | -0.644843 | 0.684665 | 0 | 1 |
| 292 | -2.266002 | 0.208427 | 0.090970 | 0.014667 | -0.927106 | 1.146918 | 2 | 1 |
| 293 | 1.473030 | 0.944250 | -0.160216 | 0.323871 | -0.664953 | 0.727193 | 1 | 1 |
| 294 | 2.116511 | 1.003706 | -1.374891 | -0.601957 | -1.760610 | 0.014541 | 1 | 1 |
| 295 | 1.516890 | 0.883674 | -1.850520 | 0.688076 | -1.350999 | 0.620360 | 1 | 1 |
| 296 | -0.171687 | 0.469515 | 0.407395 | 1.081823 | -1.053878 | -0.023872 | 2 | 1 |
| 297 | -0.023957 | 0.051075 | 0.045786 | 0.108234 | -0.643408 | 0.527902 | 2 | 1 |
| 298 | 0.152215 | 0.030843 | 0.217573 | 0.063538 | -0.471831 | 0.840207 | 2 | 1 |
| 299 | -0.286639 | 0.215830 | -0.245963 | 0.927776 | -0.474599 | 0.233343 | 2 | 1 |
| 300 | 0.411970 | 0.642559 | -0.319323 | 1.141506 | -0.291830 | 0.197165 | 1 | 1 |
| 301 | 0.915776 | 0.420715 | -0.435877 | 0.621147 | -0.746445 | 0.397040 | 1 | 1 |
| 302 | -0.963660 | -2.504276 | 0.149799 | 1.260867 | 0.289108 | -0.386784 | 3 | 1 |
| 303 | -0.732467 | -1.137228 | -0.806385 | 1.023830 | -0.646676 | -0.876828 | 3 | 1 |
| 304 | 0.175270 | -2.037232 | -1.136866 | -0.405235 | -0.294667 | 0.533651 | 1 | 1 |
| 305 | 1.507133 | -2.022655 | -0.176808 | -2.220532 | -0.028497 | 1.571387 | 1 | 1 |
| 306 | 3.112152 | -1.221959 | 0.020285 | -2.830754 | -0.654301 | 0.750704 | 1 | 1 |
| 307 | 2.013689 | -1.475971 | -1.690376 | -2.773483 | -0.813379 | 2.885407 | 1 | 1 |
| 308 | 1.511606 | 0.675622 | -0.743850 | -1.491272 | -0.807844 | -0.418377 | 1 | 1 |
| 309 | 1.412105 | 1.763606 | -0.378812 | -1.313665 | -0.185153 | -0.847850 | 0 | 1 |
| 310 | 1.122395 | 0.814854 | -0.854500 | -1.402116 | 0.114392 | -0.882142 | 0 | 1 |
| 311 | 0.336108 | 0.216775 | 0.080290 | -0.047519 | -0.756037 | -1.071728 | 3 | 1 |
| 312 | 1.201358 | 0.783381 | -0.221010 | 0.308862 | -1.206838 | -0.690837 | 1 | 1 |
| 313 | -0.672924 | -0.268212 | 1.143994 | -0.147440 | 2.008975 | -0.652644 | 0 | 1 |
| 314 | -0.034368 | 1.050289 | 0.424944 | 0.805168 | -0.376947 | -0.658873 | 2 | 1 |
315 rows × 8 columns
In [244]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[244]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b829d7d748>
In [245]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[1]))
Club De Banqueros y Empresarios¶
ANN¶
In [150]:
X = df_n_ps_std_tc[1]
In [151]:
y = df_n_ps[1]['chosen']
In [152]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [153]:
X_train.shape
Out[153]:
(191, 6)
In [154]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [155]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [156]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [253]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (20,), 'learning_rate_init': 0.002, 'max_iter': 200}, que permiten obtener un Accuracy de 78.01% y un Kappa del 31.63
Tiempo total: 31.35 minutos
In [157]:
grid.best_params_= {'activation': 'relu', 'hidden_layer_sizes': (20,), 'learning_rate_init': 0.002, 'max_iter': 200}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [158]:
input_tensor = Input(shape = (n0,))
In [159]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [160]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [161]:
model.summary()
Model: "model_8" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_9 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_24 (Dense) (None, 20) 140 _________________________________________________________________ dense_25 (Dense) (None, 1) 21 ================================================================= Total params: 161 Trainable params: 161 Non-trainable params: 0 _________________________________________________________________
In [162]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 191 samples, validate on 64 samples Epoch 1/200 191/191 [==============================] - 0s 853us/step - loss: 0.7042 - accuracy: 0.5864 - val_loss: 0.7027 - val_accuracy: 0.6094 Epoch 2/200 191/191 [==============================] - 0s 63us/step - loss: 0.6768 - accuracy: 0.6126 - val_loss: 0.6629 - val_accuracy: 0.6562 Epoch 3/200 191/191 [==============================] - 0s 58us/step - loss: 0.6551 - accuracy: 0.6545 - val_loss: 0.6287 - val_accuracy: 0.6875 Epoch 4/200 191/191 [==============================] - 0s 52us/step - loss: 0.6361 - accuracy: 0.6545 - val_loss: 0.5994 - val_accuracy: 0.6719 Epoch 5/200 191/191 [==============================] - 0s 63us/step - loss: 0.6194 - accuracy: 0.6492 - val_loss: 0.5762 - val_accuracy: 0.6719 Epoch 6/200 191/191 [==============================] - 0s 58us/step - loss: 0.6084 - accuracy: 0.6806 - val_loss: 0.5564 - val_accuracy: 0.7031 Epoch 7/200 191/191 [==============================] - 0s 58us/step - loss: 0.5988 - accuracy: 0.6859 - val_loss: 0.5405 - val_accuracy: 0.7344 Epoch 8/200 191/191 [==============================] - 0s 58us/step - loss: 0.5920 - accuracy: 0.6754 - val_loss: 0.5273 - val_accuracy: 0.7500 Epoch 9/200 191/191 [==============================] - 0s 52us/step - loss: 0.5862 - accuracy: 0.6806 - val_loss: 0.5176 - val_accuracy: 0.7500 Epoch 10/200 191/191 [==============================] - 0s 63us/step - loss: 0.5817 - accuracy: 0.6859 - val_loss: 0.5105 - val_accuracy: 0.7344 Epoch 11/200 191/191 [==============================] - 0s 63us/step - loss: 0.5775 - accuracy: 0.6911 - val_loss: 0.5053 - val_accuracy: 0.7500 Epoch 12/200 191/191 [==============================] - 0s 63us/step - loss: 0.5749 - accuracy: 0.6911 - val_loss: 0.5011 - val_accuracy: 0.7656 Epoch 13/200 191/191 [==============================] - 0s 58us/step - loss: 0.5722 - accuracy: 0.6911 - val_loss: 0.4985 - val_accuracy: 0.7656 Epoch 14/200 191/191 [==============================] - 0s 52us/step - loss: 0.5697 - accuracy: 0.6911 - val_loss: 0.4965 - val_accuracy: 0.7656 Epoch 15/200 191/191 [==============================] - 0s 63us/step - loss: 0.5678 - accuracy: 0.6911 - val_loss: 0.4948 - val_accuracy: 0.7656 Epoch 16/200 191/191 [==============================] - 0s 63us/step - loss: 0.5658 - accuracy: 0.7016 - val_loss: 0.4946 - val_accuracy: 0.7500 Epoch 17/200 191/191 [==============================] - 0s 68us/step - loss: 0.5638 - accuracy: 0.7016 - val_loss: 0.4939 - val_accuracy: 0.7500 Epoch 18/200 191/191 [==============================] - 0s 63us/step - loss: 0.5620 - accuracy: 0.7068 - val_loss: 0.4932 - val_accuracy: 0.7344 Epoch 19/200 191/191 [==============================] - 0s 63us/step - loss: 0.5604 - accuracy: 0.7016 - val_loss: 0.4928 - val_accuracy: 0.7344 Epoch 20/200 191/191 [==============================] - 0s 58us/step - loss: 0.5590 - accuracy: 0.7016 - val_loss: 0.4920 - val_accuracy: 0.7500 Epoch 21/200 191/191 [==============================] - 0s 63us/step - loss: 0.5571 - accuracy: 0.7068 - val_loss: 0.4925 - val_accuracy: 0.7500 Epoch 22/200 191/191 [==============================] - 0s 73us/step - loss: 0.5554 - accuracy: 0.7016 - val_loss: 0.4933 - val_accuracy: 0.7344 Epoch 00022: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 23/200 191/191 [==============================] - 0s 99us/step - loss: 0.5542 - accuracy: 0.7068 - val_loss: 0.4932 - val_accuracy: 0.7500 Epoch 24/200 191/191 [==============================] - 0s 68us/step - loss: 0.5533 - accuracy: 0.7068 - val_loss: 0.4938 - val_accuracy: 0.7500 Epoch 25/200 191/191 [==============================] - 0s 68us/step - loss: 0.5526 - accuracy: 0.7120 - val_loss: 0.4940 - val_accuracy: 0.7500 Epoch 26/200 191/191 [==============================] - 0s 58us/step - loss: 0.5518 - accuracy: 0.7173 - val_loss: 0.4940 - val_accuracy: 0.7500 Epoch 27/200 191/191 [==============================] - 0s 63us/step - loss: 0.5510 - accuracy: 0.7277 - val_loss: 0.4944 - val_accuracy: 0.7500 Epoch 28/200 191/191 [==============================] - 0s 68us/step - loss: 0.5502 - accuracy: 0.7277 - val_loss: 0.4949 - val_accuracy: 0.7500 Epoch 29/200 191/191 [==============================] - 0s 73us/step - loss: 0.5495 - accuracy: 0.7277 - val_loss: 0.4951 - val_accuracy: 0.7500 Epoch 30/200 191/191 [==============================] - 0s 68us/step - loss: 0.5487 - accuracy: 0.7277 - val_loss: 0.4953 - val_accuracy: 0.7500 Epoch 31/200 191/191 [==============================] - 0s 78us/step - loss: 0.5482 - accuracy: 0.7277 - val_loss: 0.4954 - val_accuracy: 0.7500 Epoch 32/200 191/191 [==============================] - 0s 68us/step - loss: 0.5475 - accuracy: 0.7225 - val_loss: 0.4960 - val_accuracy: 0.7500 Epoch 00032: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 33/200 191/191 [==============================] - 0s 63us/step - loss: 0.5467 - accuracy: 0.7173 - val_loss: 0.4960 - val_accuracy: 0.7500 Epoch 34/200 191/191 [==============================] - 0s 63us/step - loss: 0.5464 - accuracy: 0.7173 - val_loss: 0.4959 - val_accuracy: 0.7500 Epoch 35/200 191/191 [==============================] - 0s 52us/step - loss: 0.5461 - accuracy: 0.7120 - val_loss: 0.4961 - val_accuracy: 0.7500 Epoch 36/200 191/191 [==============================] - 0s 58us/step - loss: 0.5457 - accuracy: 0.7120 - val_loss: 0.4961 - val_accuracy: 0.7500 Epoch 37/200 191/191 [==============================] - 0s 63us/step - loss: 0.5453 - accuracy: 0.7068 - val_loss: 0.4964 - val_accuracy: 0.7500 Epoch 38/200 191/191 [==============================] - 0s 68us/step - loss: 0.5450 - accuracy: 0.7068 - val_loss: 0.4964 - val_accuracy: 0.7500 Epoch 39/200 191/191 [==============================] - 0s 63us/step - loss: 0.5446 - accuracy: 0.7068 - val_loss: 0.4964 - val_accuracy: 0.7500 Epoch 40/200 191/191 [==============================] - 0s 58us/step - loss: 0.5444 - accuracy: 0.7120 - val_loss: 0.4966 - val_accuracy: 0.7500 Epoch 41/200 191/191 [==============================] - 0s 52us/step - loss: 0.5439 - accuracy: 0.7173 - val_loss: 0.4965 - val_accuracy: 0.7500 Epoch 42/200 191/191 [==============================] - 0s 63us/step - loss: 0.5436 - accuracy: 0.7120 - val_loss: 0.4968 - val_accuracy: 0.7500 Epoch 00042: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 43/200 191/191 [==============================] - 0s 68us/step - loss: 0.5432 - accuracy: 0.7120 - val_loss: 0.4968 - val_accuracy: 0.7500 Epoch 44/200 191/191 [==============================] - 0s 68us/step - loss: 0.5431 - accuracy: 0.7173 - val_loss: 0.4968 - val_accuracy: 0.7500 Epoch 45/200 191/191 [==============================] - 0s 68us/step - loss: 0.5429 - accuracy: 0.7173 - val_loss: 0.4970 - val_accuracy: 0.7500 Epoch 46/200 191/191 [==============================] - 0s 68us/step - loss: 0.5427 - accuracy: 0.7173 - val_loss: 0.4970 - val_accuracy: 0.7500 Epoch 47/200 191/191 [==============================] - 0s 68us/step - loss: 0.5425 - accuracy: 0.7225 - val_loss: 0.4970 - val_accuracy: 0.7500 Epoch 48/200 191/191 [==============================] - 0s 63us/step - loss: 0.5424 - accuracy: 0.7225 - val_loss: 0.4970 - val_accuracy: 0.7500 Epoch 49/200 191/191 [==============================] - 0s 94us/step - loss: 0.5422 - accuracy: 0.7225 - val_loss: 0.4971 - val_accuracy: 0.7500 Epoch 50/200 191/191 [==============================] - 0s 89us/step - loss: 0.5420 - accuracy: 0.7225 - val_loss: 0.4970 - val_accuracy: 0.7500 Epoch 51/200 191/191 [==============================] - 0s 89us/step - loss: 0.5419 - accuracy: 0.7225 - val_loss: 0.4972 - val_accuracy: 0.7500 Epoch 52/200 191/191 [==============================] - 0s 89us/step - loss: 0.5417 - accuracy: 0.7225 - val_loss: 0.4974 - val_accuracy: 0.7500 Epoch 00052: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 53/200 191/191 [==============================] - 0s 84us/step - loss: 0.5415 - accuracy: 0.7277 - val_loss: 0.4975 - val_accuracy: 0.7500 Epoch 54/200 191/191 [==============================] - 0s 84us/step - loss: 0.5414 - accuracy: 0.7277 - val_loss: 0.4974 - val_accuracy: 0.7500 Epoch 55/200 191/191 [==============================] - 0s 115us/step - loss: 0.5413 - accuracy: 0.7277 - val_loss: 0.4974 - val_accuracy: 0.7500 Epoch 56/200 191/191 [==============================] - 0s 87us/step - loss: 0.5412 - accuracy: 0.7277 - val_loss: 0.4974 - val_accuracy: 0.7500 Epoch 57/200 191/191 [==============================] - 0s 107us/step - loss: 0.5412 - accuracy: 0.7277 - val_loss: 0.4974 - val_accuracy: 0.7500 Epoch 58/200 191/191 [==============================] - 0s 89us/step - loss: 0.5411 - accuracy: 0.7277 - val_loss: 0.4974 - val_accuracy: 0.7500 Epoch 59/200 191/191 [==============================] - 0s 78us/step - loss: 0.5410 - accuracy: 0.7277 - val_loss: 0.4975 - val_accuracy: 0.7500 Epoch 60/200 191/191 [==============================] - ETA: 0s - loss: 0.5595 - accuracy: 0.71 - 0s 78us/step - loss: 0.5409 - accuracy: 0.7277 - val_loss: 0.4975 - val_accuracy: 0.7500 Epoch 61/200 191/191 [==============================] - 0s 84us/step - loss: 0.5408 - accuracy: 0.7277 - val_loss: 0.4976 - val_accuracy: 0.7500 Epoch 62/200 191/191 [==============================] - 0s 84us/step - loss: 0.5407 - accuracy: 0.7277 - val_loss: 0.4976 - val_accuracy: 0.7500 Epoch 00062: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 63/200 191/191 [==============================] - 0s 89us/step - loss: 0.5407 - accuracy: 0.7277 - val_loss: 0.4976 - val_accuracy: 0.7500 Epoch 64/200 191/191 [==============================] - 0s 89us/step - loss: 0.5406 - accuracy: 0.7277 - val_loss: 0.4976 - val_accuracy: 0.7500 Epoch 65/200 191/191 [==============================] - 0s 89us/step - loss: 0.5406 - accuracy: 0.7277 - val_loss: 0.4976 - val_accuracy: 0.7500 Epoch 66/200 191/191 [==============================] - 0s 73us/step - loss: 0.5405 - accuracy: 0.7277 - val_loss: 0.4976 - val_accuracy: 0.7500 Epoch 67/200 191/191 [==============================] - 0s 131us/step - loss: 0.5405 - accuracy: 0.7277 - val_loss: 0.4977 - val_accuracy: 0.7500 Epoch 68/200 191/191 [==============================] - 0s 89us/step - loss: 0.5405 - accuracy: 0.7277 - val_loss: 0.4977 - val_accuracy: 0.7500 Epoch 69/200 191/191 [==============================] - 0s 89us/step - loss: 0.5404 - accuracy: 0.7277 - val_loss: 0.4977 - val_accuracy: 0.7500 Epoch 70/200 191/191 [==============================] - 0s 89us/step - loss: 0.5404 - accuracy: 0.7277 - val_loss: 0.4977 - val_accuracy: 0.7500 Epoch 71/200 191/191 [==============================] - 0s 89us/step - loss: 0.5403 - accuracy: 0.7277 - val_loss: 0.4977 - val_accuracy: 0.7500 Epoch 72/200 191/191 [==============================] - 0s 73us/step - loss: 0.5403 - accuracy: 0.7277 - val_loss: 0.4977 - val_accuracy: 0.7500 Epoch 00072: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 73/200 191/191 [==============================] - 0s 84us/step - loss: 0.5403 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 74/200 191/191 [==============================] - 0s 84us/step - loss: 0.5402 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 75/200 191/191 [==============================] - 0s 84us/step - loss: 0.5402 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 76/200 191/191 [==============================] - 0s 94us/step - loss: 0.5402 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 77/200 191/191 [==============================] - 0s 84us/step - loss: 0.5402 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 78/200 191/191 [==============================] - 0s 78us/step - loss: 0.5402 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 79/200 191/191 [==============================] - 0s 84us/step - loss: 0.5401 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 80/200 191/191 [==============================] - 0s 89us/step - loss: 0.5401 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 81/200 191/191 [==============================] - 0s 89us/step - loss: 0.5401 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 82/200 191/191 [==============================] - 0s 84us/step - loss: 0.5401 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 00082: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 83/200 191/191 [==============================] - 0s 78us/step - loss: 0.5401 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 84/200 191/191 [==============================] - 0s 78us/step - loss: 0.5401 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 85/200 191/191 [==============================] - 0s 84us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 86/200 191/191 [==============================] - 0s 99us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 87/200 191/191 [==============================] - 0s 84us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 88/200 191/191 [==============================] - 0s 94us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 89/200 191/191 [==============================] - 0s 84us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 90/200 191/191 [==============================] - 0s 110us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 91/200 191/191 [==============================] - 0s 89us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 92/200 191/191 [==============================] - 0s 84us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 00092: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 93/200 191/191 [==============================] - 0s 84us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 94/200 191/191 [==============================] - 0s 89us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 95/200 191/191 [==============================] - 0s 78us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 96/200 191/191 [==============================] - 0s 89us/step - loss: 0.5400 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 97/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 98/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4978 - val_accuracy: 0.7500 Epoch 99/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 100/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 101/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 102/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00102: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 103/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 104/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 105/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 106/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 107/200 191/191 [==============================] - 0s 110us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 108/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 109/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 110/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 111/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 112/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00112: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 113/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 114/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 115/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 116/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 117/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 118/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 119/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 120/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 121/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 122/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00122: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07. Epoch 123/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 124/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 125/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 126/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 127/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 128/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 129/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 130/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 131/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 132/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00132: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07. Epoch 133/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 134/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 135/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 136/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 137/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 138/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 139/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 140/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 141/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 142/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00142: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07. Epoch 143/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 144/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 145/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 146/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 147/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 148/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 149/200 191/191 [==============================] - 0s 115us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 150/200 191/191 [==============================] - 0s 120us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 151/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 152/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00152: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07. Epoch 153/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 154/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 155/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 156/200 191/191 [==============================] - 0s 105us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 157/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 158/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 159/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 160/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 161/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 162/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00162: ReduceLROnPlateau reducing learning rate to 6.103515914901436e-08. Epoch 163/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 164/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 165/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 166/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 167/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 168/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 169/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 170/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 171/200 191/191 [==============================] - 0s 105us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 172/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00172: ReduceLROnPlateau reducing learning rate to 3.051757957450718e-08. Epoch 173/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 174/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 175/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 176/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 177/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 178/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 179/200 191/191 [==============================] - 0s 99us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 180/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 181/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 182/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00182: ReduceLROnPlateau reducing learning rate to 1.525878978725359e-08. Epoch 183/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 184/200 191/191 [==============================] - 0s 183us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 185/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 186/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 187/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 188/200 191/191 [==============================] - 0s 105us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 189/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 190/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 191/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 192/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 00192: ReduceLROnPlateau reducing learning rate to 7.629394893626795e-09. Epoch 193/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 194/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 195/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 196/200 191/191 [==============================] - 0s 94us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 197/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 198/200 191/191 [==============================] - 0s 78us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 199/200 191/191 [==============================] - 0s 89us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500 Epoch 200/200 191/191 [==============================] - 0s 84us/step - loss: 0.5399 - accuracy: 0.7277 - val_loss: 0.4979 - val_accuracy: 0.7500
In [163]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 200)
In [164]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
64/64 [==============================] - 0s 94us/step test loss: 0.49787381291389465, test accuracy: 0.75
In [165]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6072041166380788
In [166]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.05360443622920519 [[46 7] [ 9 2]]
KMeans¶
In [264]:
X
Out[264]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 0.898091 | 0.151819 | -1.172713 | 0.474387 | -0.020230 | 1.228657 |
| 1 | 0.618513 | -0.762588 | 0.061946 | 0.944076 | 0.697880 | 0.021150 |
| 2 | 0.685649 | 0.002933 | 0.719805 | -1.251700 | -0.952424 | 1.444556 |
| 3 | 1.175209 | -0.552349 | 0.336427 | 0.482978 | -0.212146 | -0.144225 |
| 4 | 1.350337 | -1.407757 | 0.258917 | -0.523670 | 0.099306 | 1.706064 |
| 5 | 0.907564 | -1.769301 | 1.177857 | -0.869472 | 0.392594 | 0.385760 |
| 6 | -0.071420 | -0.800769 | 0.238726 | 1.318866 | -1.075628 | -0.545006 |
| 7 | 0.476433 | -1.202140 | -1.713665 | 0.379487 | -0.347674 | 0.777899 |
| 8 | 0.572039 | -1.488738 | -0.403914 | -1.066061 | -0.818836 | 0.339231 |
| 9 | 0.741137 | 0.139987 | 0.726307 | 1.670135 | -0.317435 | -1.091941 |
| 10 | 0.533655 | -0.111619 | 0.435253 | 1.832919 | -0.556933 | -1.014603 |
| 11 | -0.667308 | 0.502566 | -1.137726 | -0.714521 | -0.497571 | 0.123297 |
| 12 | 0.161812 | 0.294263 | 0.659166 | -0.336211 | 1.410350 | -0.272418 |
| 13 | -0.373777 | -1.439681 | 0.009190 | 0.731635 | 0.138615 | 0.850511 |
| 14 | 0.745550 | 0.214669 | 0.209787 | 0.424963 | 0.448908 | -0.204578 |
| 15 | 0.320726 | 0.108060 | 0.208510 | -1.138882 | -0.874041 | -1.779091 |
| 16 | 0.646392 | -0.726119 | 0.153724 | -0.203580 | -1.017329 | -1.068601 |
| 17 | -0.042981 | -0.672256 | 0.358250 | -0.385808 | -0.341018 | -1.823744 |
| 18 | 0.822192 | 0.184879 | 1.658679 | 1.705929 | 3.070140 | -1.218005 |
| 19 | 0.175070 | 0.195153 | 1.969940 | 0.005043 | 0.430538 | -1.502715 |
| 20 | 1.339692 | -1.202498 | 0.487937 | -0.769520 | -1.973308 | -0.400699 |
| 21 | 1.290923 | -0.546138 | 0.120024 | 0.429258 | -0.165681 | 0.856938 |
| 22 | 1.528224 | -0.912727 | 0.962682 | -0.386673 | -0.772181 | -0.291766 |
| 23 | -0.486779 | -1.124424 | 0.559106 | 0.746533 | -1.101240 | 1.082216 |
| 24 | -0.230729 | 0.999926 | -0.678209 | -0.175670 | 1.412258 | 0.572372 |
| 25 | -0.632681 | 0.618852 | -0.778803 | -0.808112 | -0.442115 | -0.146177 |
| 26 | -1.151505 | -1.127449 | 1.500641 | -0.822825 | 0.158380 | 0.792656 |
| 27 | 0.265739 | -3.078847 | -0.939567 | 0.268673 | -0.642098 | -0.984495 |
| 28 | 0.623357 | -1.241561 | -1.149654 | 1.231993 | 2.023015 | -0.070476 |
| 29 | 0.930863 | -1.763587 | -1.608926 | 0.462097 | -0.677599 | -0.693427 |
| ... | ... | ... | ... | ... | ... | ... |
| 225 | -1.444140 | -0.088370 | -0.458428 | 0.530251 | -0.475625 | -0.057486 |
| 226 | -0.297006 | 0.887935 | 0.467148 | 2.000374 | -0.396849 | -0.846195 |
| 227 | -1.624166 | 0.777486 | 0.635044 | -1.376180 | 0.998008 | -0.910882 |
| 228 | 0.230618 | 1.438780 | 0.301556 | -1.353873 | -0.586627 | -0.102947 |
| 229 | -0.163123 | 1.329205 | 0.721279 | -1.383030 | 0.540446 | -1.181571 |
| 230 | -1.337576 | 0.249897 | 0.081067 | 0.886335 | -0.078090 | -0.344245 |
| 231 | 0.304553 | 0.584052 | 0.915910 | 2.455180 | 1.007231 | 0.268298 |
| 232 | -0.291785 | 0.247731 | -0.740382 | 0.896773 | 0.457951 | 0.390640 |
| 233 | -0.532056 | 1.686101 | 0.358185 | -1.561985 | 0.911246 | 0.638759 |
| 234 | -1.223692 | 0.723005 | 0.599197 | -0.955626 | 0.653814 | 0.112686 |
| 235 | 1.412552 | -0.817418 | 0.038464 | -2.397710 | -2.903923 | 1.454325 |
| 236 | 0.141392 | -0.756740 | -1.981390 | -0.636588 | 0.230786 | 0.968907 |
| 237 | 1.157567 | -0.442417 | -1.342532 | -0.893118 | -0.552517 | -0.791388 |
| 238 | -1.683225 | -0.036571 | 0.297162 | -1.488549 | 1.387872 | -0.306946 |
| 239 | -0.997159 | 0.655257 | 2.239993 | -1.422875 | 0.373101 | 0.159004 |
| 240 | -1.142741 | 0.931927 | 1.440876 | 0.665641 | -0.994237 | -1.093039 |
| 241 | -0.151675 | -0.971306 | 0.447819 | 0.895444 | -0.863907 | 0.150120 |
| 242 | -0.837654 | -1.170592 | 0.622658 | 0.448216 | -0.830715 | -0.222067 |
| 243 | -0.059101 | -0.857751 | 0.253657 | 0.272951 | -0.833270 | 0.160823 |
| 244 | 1.455210 | -1.123798 | 1.124970 | -1.841854 | -0.183521 | -0.193778 |
| 245 | 1.459407 | -1.071308 | -0.261053 | -0.731205 | 0.603463 | 0.358072 |
| 246 | 1.850117 | -1.364586 | 1.015519 | -1.479941 | -1.262489 | -0.485304 |
| 247 | 0.468703 | 0.776904 | -1.200084 | -0.109459 | 0.572206 | 0.353229 |
| 248 | 0.758187 | -0.030802 | -1.190930 | -0.092637 | 0.048267 | 2.174173 |
| 249 | 0.465492 | -0.042081 | 0.541343 | 0.584645 | 0.066443 | -1.886670 |
| 250 | -1.114193 | 1.666162 | 0.201458 | -1.543125 | -0.123758 | -0.430641 |
| 251 | -1.675129 | 1.101864 | 0.721966 | -1.964153 | 0.827116 | 0.134812 |
| 252 | -1.371728 | 0.888874 | -0.186673 | -0.931346 | 0.795500 | -1.063218 |
| 253 | 0.221249 | 0.272024 | -1.593712 | -0.242394 | 0.752955 | 1.102656 |
| 254 | -0.747040 | 1.308435 | 0.858494 | -1.950134 | 1.779312 | -0.711789 |
255 rows × 6 columns
In [265]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[265]:
[1530.0000000000002, 1266.8988304034983, 1085.4171102625123, 963.5827926636907, 872.5239995069635, 797.6140851961846, 747.1323294070899, 703.670300371115, 664.3614627122823, 637.5590430281768, 607.7011770650902, 585.4389967082509, 558.8506960652073, 540.5660329891642]
In [266]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[266]:
[<matplotlib.lines.Line2D at 0x1b829e4f2e8>]
K=3
In [267]:
kmeans_tc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[267]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [268]:
kmeans_tc.labels_
Out[268]:
array([2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2,
0, 0, 1, 1, 0, 0, 0, 2, 2, 0, 0, 1, 1, 0, 2, 2, 2, 2, 2, 0, 0, 0,
0, 2, 2, 0, 1, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0,
1, 1, 1, 1, 1, 0, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 2, 0, 1, 0, 1,
1, 1, 2, 2, 2, 0, 0, 0, 2, 1, 2, 1, 0, 2, 0, 2, 2, 1, 1, 2, 1, 1,
1, 0, 1, 2, 0, 2, 2, 1, 1, 1, 2, 2, 0, 2, 2, 2, 1, 0, 2, 1, 0, 0,
2, 2, 2, 2, 1, 1, 1, 2, 0, 0, 0, 0, 1, 0, 2, 1, 2, 2, 2, 2, 1, 1,
1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 2,
1, 2, 0, 1, 1, 2, 2, 2, 2, 0, 0, 2, 2, 1, 0, 2, 1, 1, 1, 2, 1, 2,
2, 2, 1, 1, 2, 2, 1, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 2, 1, 1, 2, 2, 2, 1, 1, 1, 0,
0, 0, 0, 2, 0, 2, 2, 0, 1, 1, 1, 2, 1])
In [269]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[269]:
array([2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2,
0, 0, 1, 1, 0, 0, 0, 2, 2, 0, 0, 1, 1, 0, 2, 2, 2, 2, 2, 0, 0, 0,
0, 2, 2, 0, 1, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0,
1, 1, 1, 1, 1, 0, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 2, 0, 1, 0, 1,
1, 1, 2, 2, 2, 0, 0, 0, 2, 1, 2, 1, 0, 2, 0, 2, 2, 1, 1, 2, 1, 1,
1, 0, 1, 2, 0, 2, 2, 1, 1, 1, 2, 2, 0, 2, 2, 2, 1, 0, 2, 1, 0, 0,
2, 2, 2, 2, 1, 1, 1, 2, 0, 0, 0, 0, 1, 0, 2, 1, 2, 2, 2, 2, 1, 1,
1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 2,
1, 2, 0, 1, 1, 2, 2, 2, 2, 0, 0, 2, 2, 1, 0, 2, 1, 1, 1, 2, 1, 2,
2, 2, 1, 1, 2, 2, 1, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 2, 1, 1, 2, 2, 2, 1, 1, 1, 0,
0, 0, 0, 2, 0, 2, 2, 0, 1, 1, 1, 2, 1])
In [270]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [271]:
X
Out[271]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.898091 | 0.151819 | -1.172713 | 0.474387 | -0.020230 | 1.228657 | 2 | 0 |
| 1 | 0.618513 | -0.762588 | 0.061946 | 0.944076 | 0.697880 | 0.021150 | 0 | 0 |
| 2 | 0.685649 | 0.002933 | 0.719805 | -1.251700 | -0.952424 | 1.444556 | 2 | 0 |
| 3 | 1.175209 | -0.552349 | 0.336427 | 0.482978 | -0.212146 | -0.144225 | 0 | 0 |
| 4 | 1.350337 | -1.407757 | 0.258917 | -0.523670 | 0.099306 | 1.706064 | 2 | 0 |
| 5 | 0.907564 | -1.769301 | 1.177857 | -0.869472 | 0.392594 | 0.385760 | 0 | 0 |
| 6 | -0.071420 | -0.800769 | 0.238726 | 1.318866 | -1.075628 | -0.545006 | 0 | 0 |
| 7 | 0.476433 | -1.202140 | -1.713665 | 0.379487 | -0.347674 | 0.777899 | 2 | 0 |
| 8 | 0.572039 | -1.488738 | -0.403914 | -1.066061 | -0.818836 | 0.339231 | 2 | 0 |
| 9 | 0.741137 | 0.139987 | 0.726307 | 1.670135 | -0.317435 | -1.091941 | 0 | 0 |
| 10 | 0.533655 | -0.111619 | 0.435253 | 1.832919 | -0.556933 | -1.014603 | 0 | 0 |
| 11 | -0.667308 | 0.502566 | -1.137726 | -0.714521 | -0.497571 | 0.123297 | 2 | 0 |
| 12 | 0.161812 | 0.294263 | 0.659166 | -0.336211 | 1.410350 | -0.272418 | 1 | 0 |
| 13 | -0.373777 | -1.439681 | 0.009190 | 0.731635 | 0.138615 | 0.850511 | 0 | 0 |
| 14 | 0.745550 | 0.214669 | 0.209787 | 0.424963 | 0.448908 | -0.204578 | 0 | 0 |
| 15 | 0.320726 | 0.108060 | 0.208510 | -1.138882 | -0.874041 | -1.779091 | 1 | 0 |
| 16 | 0.646392 | -0.726119 | 0.153724 | -0.203580 | -1.017329 | -1.068601 | 0 | 0 |
| 17 | -0.042981 | -0.672256 | 0.358250 | -0.385808 | -0.341018 | -1.823744 | 0 | 0 |
| 18 | 0.822192 | 0.184879 | 1.658679 | 1.705929 | 3.070140 | -1.218005 | 0 | 0 |
| 19 | 0.175070 | 0.195153 | 1.969940 | 0.005043 | 0.430538 | -1.502715 | 0 | 0 |
| 20 | 1.339692 | -1.202498 | 0.487937 | -0.769520 | -1.973308 | -0.400699 | 0 | 0 |
| 21 | 1.290923 | -0.546138 | 0.120024 | 0.429258 | -0.165681 | 0.856938 | 2 | 0 |
| 22 | 1.528224 | -0.912727 | 0.962682 | -0.386673 | -0.772181 | -0.291766 | 0 | 0 |
| 23 | -0.486779 | -1.124424 | 0.559106 | 0.746533 | -1.101240 | 1.082216 | 0 | 0 |
| 24 | -0.230729 | 0.999926 | -0.678209 | -0.175670 | 1.412258 | 0.572372 | 1 | 0 |
| 25 | -0.632681 | 0.618852 | -0.778803 | -0.808112 | -0.442115 | -0.146177 | 1 | 0 |
| 26 | -1.151505 | -1.127449 | 1.500641 | -0.822825 | 0.158380 | 0.792656 | 0 | 0 |
| 27 | 0.265739 | -3.078847 | -0.939567 | 0.268673 | -0.642098 | -0.984495 | 0 | 0 |
| 28 | 0.623357 | -1.241561 | -1.149654 | 1.231993 | 2.023015 | -0.070476 | 0 | 0 |
| 29 | 0.930863 | -1.763587 | -1.608926 | 0.462097 | -0.677599 | -0.693427 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | -1.444140 | -0.088370 | -0.458428 | 0.530251 | -0.475625 | -0.057486 | 1 | 1 |
| 226 | -0.297006 | 0.887935 | 0.467148 | 2.000374 | -0.396849 | -0.846195 | 0 | 1 |
| 227 | -1.624166 | 0.777486 | 0.635044 | -1.376180 | 0.998008 | -0.910882 | 1 | 1 |
| 228 | 0.230618 | 1.438780 | 0.301556 | -1.353873 | -0.586627 | -0.102947 | 1 | 1 |
| 229 | -0.163123 | 1.329205 | 0.721279 | -1.383030 | 0.540446 | -1.181571 | 1 | 1 |
| 230 | -1.337576 | 0.249897 | 0.081067 | 0.886335 | -0.078090 | -0.344245 | 1 | 1 |
| 231 | 0.304553 | 0.584052 | 0.915910 | 2.455180 | 1.007231 | 0.268298 | 0 | 1 |
| 232 | -0.291785 | 0.247731 | -0.740382 | 0.896773 | 0.457951 | 0.390640 | 2 | 1 |
| 233 | -0.532056 | 1.686101 | 0.358185 | -1.561985 | 0.911246 | 0.638759 | 1 | 1 |
| 234 | -1.223692 | 0.723005 | 0.599197 | -0.955626 | 0.653814 | 0.112686 | 1 | 1 |
| 235 | 1.412552 | -0.817418 | 0.038464 | -2.397710 | -2.903923 | 1.454325 | 2 | 1 |
| 236 | 0.141392 | -0.756740 | -1.981390 | -0.636588 | 0.230786 | 0.968907 | 2 | 1 |
| 237 | 1.157567 | -0.442417 | -1.342532 | -0.893118 | -0.552517 | -0.791388 | 2 | 1 |
| 238 | -1.683225 | -0.036571 | 0.297162 | -1.488549 | 1.387872 | -0.306946 | 1 | 1 |
| 239 | -0.997159 | 0.655257 | 2.239993 | -1.422875 | 0.373101 | 0.159004 | 1 | 1 |
| 240 | -1.142741 | 0.931927 | 1.440876 | 0.665641 | -0.994237 | -1.093039 | 1 | 1 |
| 241 | -0.151675 | -0.971306 | 0.447819 | 0.895444 | -0.863907 | 0.150120 | 0 | 1 |
| 242 | -0.837654 | -1.170592 | 0.622658 | 0.448216 | -0.830715 | -0.222067 | 0 | 1 |
| 243 | -0.059101 | -0.857751 | 0.253657 | 0.272951 | -0.833270 | 0.160823 | 0 | 1 |
| 244 | 1.455210 | -1.123798 | 1.124970 | -1.841854 | -0.183521 | -0.193778 | 0 | 1 |
| 245 | 1.459407 | -1.071308 | -0.261053 | -0.731205 | 0.603463 | 0.358072 | 2 | 1 |
| 246 | 1.850117 | -1.364586 | 1.015519 | -1.479941 | -1.262489 | -0.485304 | 0 | 1 |
| 247 | 0.468703 | 0.776904 | -1.200084 | -0.109459 | 0.572206 | 0.353229 | 2 | 1 |
| 248 | 0.758187 | -0.030802 | -1.190930 | -0.092637 | 0.048267 | 2.174173 | 2 | 1 |
| 249 | 0.465492 | -0.042081 | 0.541343 | 0.584645 | 0.066443 | -1.886670 | 0 | 1 |
| 250 | -1.114193 | 1.666162 | 0.201458 | -1.543125 | -0.123758 | -0.430641 | 1 | 1 |
| 251 | -1.675129 | 1.101864 | 0.721966 | -1.964153 | 0.827116 | 0.134812 | 1 | 1 |
| 252 | -1.371728 | 0.888874 | -0.186673 | -0.931346 | 0.795500 | -1.063218 | 1 | 1 |
| 253 | 0.221249 | 0.272024 | -1.593712 | -0.242394 | 0.752955 | 1.102656 | 2 | 1 |
| 254 | -0.747040 | 1.308435 | 0.858494 | -1.950134 | 1.779312 | -0.711789 | 1 | 1 |
255 rows × 8 columns
In [272]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[272]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b829fb8470>
In [273]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[2]))
Gramma¶
ANN¶
In [167]:
X = df_n_ps_std_tc[2]
In [168]:
y = df_n_ps[2]['chosen']
In [169]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [170]:
X_train.shape
Out[170]:
(162, 6)
In [171]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [172]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [173]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [281]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.004, 'max_iter': 20}, que permiten obtener un Accuracy de 75.31% y un Kappa del 17.51
Tiempo total: 35.80 minutos
In [174]:
grid.best_params_={'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.004, 'max_iter': 20}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [175]:
input_tensor = Input(shape = (n0,))
In [176]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [177]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [178]:
model.summary()
Model: "model_9" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_10 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_26 (Dense) (None, 10) 70 _________________________________________________________________ dense_27 (Dense) (None, 1) 11 ================================================================= Total params: 81 Trainable params: 81 Non-trainable params: 0 _________________________________________________________________
In [179]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 162 samples, validate on 54 samples Epoch 1/20 162/162 [==============================] - 0s 1ms/step - loss: 0.7228 - accuracy: 0.6049 - val_loss: 0.7926 - val_accuracy: 0.6111 Epoch 2/20 162/162 [==============================] - 0s 74us/step - loss: 0.6740 - accuracy: 0.5988 - val_loss: 0.7654 - val_accuracy: 0.5741 Epoch 3/20 162/162 [==============================] - 0s 68us/step - loss: 0.6433 - accuracy: 0.6235 - val_loss: 0.7441 - val_accuracy: 0.5556 Epoch 4/20 162/162 [==============================] - 0s 62us/step - loss: 0.6202 - accuracy: 0.6420 - val_loss: 0.7311 - val_accuracy: 0.6111 Epoch 5/20 162/162 [==============================] - 0s 74us/step - loss: 0.6033 - accuracy: 0.6667 - val_loss: 0.7230 - val_accuracy: 0.6111 Epoch 6/20 162/162 [==============================] - 0s 68us/step - loss: 0.5913 - accuracy: 0.6914 - val_loss: 0.7176 - val_accuracy: 0.6296 Epoch 7/20 162/162 [==============================] - 0s 74us/step - loss: 0.5812 - accuracy: 0.6790 - val_loss: 0.7122 - val_accuracy: 0.6296 Epoch 8/20 162/162 [==============================] - 0s 74us/step - loss: 0.5741 - accuracy: 0.7037 - val_loss: 0.7077 - val_accuracy: 0.6296 Epoch 9/20 162/162 [==============================] - 0s 68us/step - loss: 0.5698 - accuracy: 0.7037 - val_loss: 0.7029 - val_accuracy: 0.6111 Epoch 10/20 162/162 [==============================] - 0s 62us/step - loss: 0.5642 - accuracy: 0.7037 - val_loss: 0.6976 - val_accuracy: 0.6296 Epoch 11/20 162/162 [==============================] - 0s 80us/step - loss: 0.5621 - accuracy: 0.7037 - val_loss: 0.6935 - val_accuracy: 0.6296 Epoch 12/20 162/162 [==============================] - 0s 80us/step - loss: 0.5597 - accuracy: 0.7037 - val_loss: 0.6922 - val_accuracy: 0.6296 Epoch 13/20 162/162 [==============================] - 0s 74us/step - loss: 0.5576 - accuracy: 0.7037 - val_loss: 0.6911 - val_accuracy: 0.6296 Epoch 14/20 162/162 [==============================] - 0s 68us/step - loss: 0.5555 - accuracy: 0.7037 - val_loss: 0.6896 - val_accuracy: 0.6296 Epoch 15/20 162/162 [==============================] - 0s 62us/step - loss: 0.5526 - accuracy: 0.7099 - val_loss: 0.6879 - val_accuracy: 0.6296 Epoch 16/20 162/162 [==============================] - 0s 80us/step - loss: 0.5515 - accuracy: 0.7160 - val_loss: 0.6827 - val_accuracy: 0.6111 Epoch 00016: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 17/20 162/162 [==============================] - 0s 111us/step - loss: 0.5487 - accuracy: 0.7160 - val_loss: 0.6800 - val_accuracy: 0.6111 Epoch 18/20 162/162 [==============================] - 0s 99us/step - loss: 0.5471 - accuracy: 0.7099 - val_loss: 0.6782 - val_accuracy: 0.6111 Epoch 19/20 162/162 [==============================] - 0s 86us/step - loss: 0.5465 - accuracy: 0.7160 - val_loss: 0.6764 - val_accuracy: 0.6111 Epoch 20/20 162/162 [==============================] - 0s 80us/step - loss: 0.5455 - accuracy: 0.7160 - val_loss: 0.6744 - val_accuracy: 0.6111
In [180]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 20)
In [181]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
54/54 [==============================] - 0s 74us/step test loss: 0.6743692181728504, test accuracy: 0.6111111044883728
In [182]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.5833333333333334
In [183]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: -0.10526315789473695 [[33 3] [18 0]]
KMeans¶
In [292]:
X
Out[292]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 0.618349 | 0.564005 | 0.091611 | 1.885208 | 0.563756 | 1.803892 |
| 1 | -0.149103 | -1.303101 | -0.498811 | 0.416525 | -0.660751 | -1.179350 |
| 2 | -1.141294 | -1.317570 | 0.575363 | -1.560836 | 0.336340 | 1.197709 |
| 3 | -0.988346 | -0.540855 | 1.006800 | -0.214650 | -0.364940 | 0.068869 |
| 4 | -0.640925 | -0.228256 | 0.461986 | -1.274446 | -1.494581 | -0.125833 |
| 5 | 0.651809 | -1.155045 | -1.823509 | -0.572302 | -0.125839 | 1.114927 |
| 6 | 0.451997 | -0.464785 | -1.036654 | 0.334704 | -0.130396 | 0.630280 |
| 7 | -0.013544 | -0.797079 | -0.417203 | 0.331839 | 0.387259 | -0.274179 |
| 8 | 0.970875 | 1.445790 | 1.566960 | 0.586074 | -0.399408 | 0.012962 |
| 9 | 0.807403 | 1.144660 | -0.144756 | 0.683601 | -0.910010 | 1.250279 |
| 10 | 0.694252 | 1.111013 | -0.298027 | 1.482108 | 0.290008 | 2.378749 |
| 11 | 0.812756 | 1.601196 | 0.779411 | -0.137772 | -0.049154 | -0.449505 |
| 12 | 0.108497 | 1.484673 | -1.479839 | -0.228556 | -1.160260 | -0.264037 |
| 13 | -0.204479 | 1.323672 | 0.724265 | -0.881555 | 0.971039 | -2.057333 |
| 14 | -1.530107 | -0.193077 | -1.866283 | -1.078546 | 1.564324 | 0.948469 |
| 15 | -1.432547 | 0.905544 | -0.826578 | -0.162205 | 0.225217 | -1.043164 |
| 16 | -1.690034 | 0.636522 | -1.777337 | 0.332901 | 1.464282 | -1.494455 |
| 17 | 1.518335 | 0.937667 | 2.799725 | 0.433386 | 0.422216 | -1.047176 |
| 18 | 0.684979 | 0.531321 | 1.125361 | -1.158059 | 0.870596 | -1.396783 |
| 19 | 1.308654 | 0.456382 | 1.782806 | 2.045171 | 0.583938 | 0.553846 |
| 20 | 0.778796 | -0.726044 | 0.453225 | -0.157245 | 0.172772 | -0.367794 |
| 21 | 0.504756 | -0.482434 | -0.040754 | 0.332550 | 0.158974 | 0.598871 |
| 22 | 0.781394 | -0.663174 | 0.549780 | -0.440383 | -1.459304 | -0.365324 |
| 23 | 0.146289 | 1.536025 | 0.055630 | -0.514739 | 0.417498 | -0.136146 |
| 24 | 0.355919 | 0.841372 | 0.955924 | -0.037753 | -0.158570 | -0.320586 |
| 25 | 0.037665 | 1.886176 | 0.119317 | 0.033897 | 0.626855 | -0.100332 |
| 26 | -1.466288 | 0.559979 | 0.806653 | -1.415049 | 1.973619 | 0.460540 |
| 27 | -0.476036 | 0.236137 | 1.017872 | 1.228768 | -0.501363 | -0.256857 |
| 28 | -0.005095 | -2.023777 | -0.849806 | 0.691870 | 0.593777 | 0.117392 |
| 29 | -1.739289 | 0.517216 | -0.791108 | 0.066990 | 1.730562 | -0.382581 |
| ... | ... | ... | ... | ... | ... | ... |
| 186 | -0.209907 | -0.890861 | -1.480749 | 1.818309 | -0.529062 | -0.274082 |
| 187 | 0.272964 | -1.573686 | -1.429667 | -0.280854 | 1.188917 | 0.951888 |
| 188 | -0.887693 | 0.489524 | 0.498612 | 0.954119 | -0.098669 | 0.105317 |
| 189 | -1.002974 | 0.451334 | 0.382768 | 0.817178 | -0.070872 | -0.338093 |
| 190 | -1.292658 | 0.953945 | 0.850772 | 0.522089 | 0.080980 | -0.212195 |
| 191 | 1.459775 | 0.362402 | 1.602060 | 0.094609 | 1.169480 | -0.588678 |
| 192 | 1.923340 | 0.915773 | 1.920236 | 1.121678 | 0.547246 | 0.737895 |
| 193 | 1.454162 | 0.509817 | 1.761284 | 0.561861 | 1.164134 | -0.911890 |
| 194 | 1.480242 | -0.198385 | -0.064509 | -0.994178 | -1.627007 | -0.646308 |
| 195 | 1.049366 | -0.397640 | 0.632599 | -0.627315 | -1.290501 | -0.733029 |
| 196 | 1.338278 | -0.473726 | 0.747138 | -0.872384 | -1.426898 | -0.885808 |
| 197 | 0.405835 | 1.250358 | -0.440807 | 0.810338 | -0.858610 | 1.349151 |
| 198 | 0.181164 | 0.930372 | -0.282564 | 0.193104 | -0.467723 | 0.358204 |
| 199 | 0.670208 | 1.198906 | -0.303904 | -0.039900 | -0.757903 | 0.813867 |
| 200 | -0.765252 | -0.882395 | 0.316745 | 1.160239 | -0.216695 | -0.363017 |
| 201 | -0.362519 | -1.407213 | -1.115582 | 0.682467 | 1.234600 | 0.157507 |
| 202 | 0.146885 | -1.935391 | 0.147533 | 0.380456 | 2.154532 | 1.489885 |
| 203 | 0.828916 | -0.454682 | -0.332837 | 0.712552 | 0.806644 | 0.818245 |
| 204 | 0.763529 | -1.226092 | 0.321224 | -0.239370 | -1.883123 | 0.931987 |
| 205 | 1.016504 | -0.832857 | -0.443373 | -0.082946 | 0.382222 | 0.809814 |
| 206 | 0.227287 | -0.956642 | -0.917348 | -0.459838 | -0.185694 | -0.045102 |
| 207 | 0.673549 | -0.883374 | -1.272948 | 0.092401 | 0.071859 | -0.695644 |
| 208 | 0.720898 | -1.292898 | -0.751577 | -0.256872 | 0.665717 | 0.628246 |
| 209 | -0.792808 | 0.902811 | 0.937273 | 1.790379 | 0.060934 | 0.443300 |
| 210 | -1.327416 | 1.434383 | 2.722415 | 1.254539 | 0.971727 | 1.122808 |
| 211 | -0.784226 | 0.939410 | 1.388065 | 1.619603 | -0.624808 | 0.083838 |
| 212 | -1.373692 | 0.720443 | -0.358383 | 0.586952 | -0.315282 | -0.239589 |
| 213 | -1.288536 | 0.664754 | -0.497221 | 0.792405 | -0.824437 | 0.490092 |
| 214 | 0.335584 | -0.975748 | -1.771014 | 0.289243 | 2.297665 | 0.370714 |
| 215 | 0.882521 | 2.087302 | -1.396329 | -2.436266 | -0.577825 | -1.293233 |
216 rows × 6 columns
In [293]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[293]:
[1296.0000000000002, 1080.8577066903854, 933.0740864307027, 818.2780016547574, 739.8707939120957, 660.7782107753561, 616.9452004899513, 572.5504610784069, 524.3523691729067, 492.5848935457627, 457.9596509280891, 439.47748328257137, 422.97179760296183, 407.2321746861259]
In [294]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[294]:
[<matplotlib.lines.Line2D at 0x1b82cc45550>]
K=4
In [295]:
kmeans_tc = KMeans(n_clusters=4, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[295]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [296]:
kmeans_tc.labels_
Out[296]:
array([3, 1, 3, 1, 2, 3, 3, 3, 2, 3, 3, 2, 0, 0, 0, 0, 0, 2, 2, 1, 2, 3,
2, 0, 2, 0, 0, 1, 3, 0, 3, 3, 3, 1, 0, 3, 3, 3, 1, 1, 1, 3, 3, 3,
3, 3, 1, 2, 2, 2, 1, 1, 1, 0, 0, 0, 3, 3, 1, 3, 0, 1, 0, 3, 3, 1,
2, 1, 1, 1, 1, 2, 2, 2, 0, 0, 3, 1, 0, 1, 3, 3, 3, 0, 1, 0, 2, 2,
2, 3, 0, 0, 0, 3, 1, 1, 1, 1, 3, 3, 3, 2, 0, 1, 1, 1, 3, 3, 0, 3,
0, 2, 2, 2, 3, 0, 0, 1, 0, 0, 2, 2, 0, 1, 1, 1, 1, 1, 1, 3, 3, 2,
2, 2, 3, 0, 1, 3, 1, 1, 0, 0, 0, 1, 0, 1, 1, 2, 2, 2, 2, 2, 2, 3,
2, 3, 2, 1, 3, 3, 0, 1, 3, 3, 3, 0, 1, 0, 2, 3, 3, 0, 0, 3, 0, 0,
0, 2, 3, 3, 0, 1, 1, 3, 1, 0, 1, 3, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3,
3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 0])
In [297]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[297]:
array([3, 1, 3, 1, 2, 3, 3, 3, 2, 3, 3, 2, 0, 0, 0, 0, 0, 2, 2, 1, 2, 3,
2, 0, 2, 0, 0, 1, 3, 0, 3, 3, 3, 1, 0, 3, 3, 3, 1, 1, 1, 3, 3, 3,
3, 3, 1, 2, 2, 2, 1, 1, 1, 0, 0, 0, 3, 3, 1, 3, 0, 1, 0, 3, 3, 1,
2, 1, 1, 1, 1, 2, 2, 2, 0, 0, 3, 1, 0, 1, 3, 3, 3, 0, 1, 0, 2, 2,
2, 3, 0, 0, 0, 3, 1, 1, 1, 1, 3, 3, 3, 2, 0, 1, 1, 1, 3, 3, 0, 3,
0, 2, 2, 2, 3, 0, 0, 1, 0, 0, 2, 2, 0, 1, 1, 1, 1, 1, 1, 3, 3, 2,
2, 2, 3, 0, 1, 3, 1, 1, 0, 0, 0, 1, 0, 1, 1, 2, 2, 2, 2, 2, 2, 3,
2, 3, 2, 1, 3, 3, 0, 1, 3, 3, 3, 0, 1, 0, 2, 3, 3, 0, 0, 3, 0, 0,
0, 2, 3, 3, 0, 1, 1, 3, 1, 0, 1, 3, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3,
3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 0])
In [298]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [299]:
X
Out[299]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.618349 | 0.564005 | 0.091611 | 1.885208 | 0.563756 | 1.803892 | 3 | 0 |
| 1 | -0.149103 | -1.303101 | -0.498811 | 0.416525 | -0.660751 | -1.179350 | 1 | 0 |
| 2 | -1.141294 | -1.317570 | 0.575363 | -1.560836 | 0.336340 | 1.197709 | 3 | 0 |
| 3 | -0.988346 | -0.540855 | 1.006800 | -0.214650 | -0.364940 | 0.068869 | 1 | 0 |
| 4 | -0.640925 | -0.228256 | 0.461986 | -1.274446 | -1.494581 | -0.125833 | 2 | 0 |
| 5 | 0.651809 | -1.155045 | -1.823509 | -0.572302 | -0.125839 | 1.114927 | 3 | 0 |
| 6 | 0.451997 | -0.464785 | -1.036654 | 0.334704 | -0.130396 | 0.630280 | 3 | 0 |
| 7 | -0.013544 | -0.797079 | -0.417203 | 0.331839 | 0.387259 | -0.274179 | 3 | 0 |
| 8 | 0.970875 | 1.445790 | 1.566960 | 0.586074 | -0.399408 | 0.012962 | 2 | 0 |
| 9 | 0.807403 | 1.144660 | -0.144756 | 0.683601 | -0.910010 | 1.250279 | 3 | 0 |
| 10 | 0.694252 | 1.111013 | -0.298027 | 1.482108 | 0.290008 | 2.378749 | 3 | 0 |
| 11 | 0.812756 | 1.601196 | 0.779411 | -0.137772 | -0.049154 | -0.449505 | 2 | 0 |
| 12 | 0.108497 | 1.484673 | -1.479839 | -0.228556 | -1.160260 | -0.264037 | 0 | 0 |
| 13 | -0.204479 | 1.323672 | 0.724265 | -0.881555 | 0.971039 | -2.057333 | 0 | 0 |
| 14 | -1.530107 | -0.193077 | -1.866283 | -1.078546 | 1.564324 | 0.948469 | 0 | 0 |
| 15 | -1.432547 | 0.905544 | -0.826578 | -0.162205 | 0.225217 | -1.043164 | 0 | 0 |
| 16 | -1.690034 | 0.636522 | -1.777337 | 0.332901 | 1.464282 | -1.494455 | 0 | 0 |
| 17 | 1.518335 | 0.937667 | 2.799725 | 0.433386 | 0.422216 | -1.047176 | 2 | 0 |
| 18 | 0.684979 | 0.531321 | 1.125361 | -1.158059 | 0.870596 | -1.396783 | 2 | 0 |
| 19 | 1.308654 | 0.456382 | 1.782806 | 2.045171 | 0.583938 | 0.553846 | 1 | 0 |
| 20 | 0.778796 | -0.726044 | 0.453225 | -0.157245 | 0.172772 | -0.367794 | 2 | 0 |
| 21 | 0.504756 | -0.482434 | -0.040754 | 0.332550 | 0.158974 | 0.598871 | 3 | 0 |
| 22 | 0.781394 | -0.663174 | 0.549780 | -0.440383 | -1.459304 | -0.365324 | 2 | 0 |
| 23 | 0.146289 | 1.536025 | 0.055630 | -0.514739 | 0.417498 | -0.136146 | 0 | 0 |
| 24 | 0.355919 | 0.841372 | 0.955924 | -0.037753 | -0.158570 | -0.320586 | 2 | 0 |
| 25 | 0.037665 | 1.886176 | 0.119317 | 0.033897 | 0.626855 | -0.100332 | 0 | 0 |
| 26 | -1.466288 | 0.559979 | 0.806653 | -1.415049 | 1.973619 | 0.460540 | 0 | 0 |
| 27 | -0.476036 | 0.236137 | 1.017872 | 1.228768 | -0.501363 | -0.256857 | 1 | 0 |
| 28 | -0.005095 | -2.023777 | -0.849806 | 0.691870 | 0.593777 | 0.117392 | 3 | 0 |
| 29 | -1.739289 | 0.517216 | -0.791108 | 0.066990 | 1.730562 | -0.382581 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | -0.209907 | -0.890861 | -1.480749 | 1.818309 | -0.529062 | -0.274082 | 1 | 1 |
| 187 | 0.272964 | -1.573686 | -1.429667 | -0.280854 | 1.188917 | 0.951888 | 3 | 1 |
| 188 | -0.887693 | 0.489524 | 0.498612 | 0.954119 | -0.098669 | 0.105317 | 1 | 1 |
| 189 | -1.002974 | 0.451334 | 0.382768 | 0.817178 | -0.070872 | -0.338093 | 1 | 1 |
| 190 | -1.292658 | 0.953945 | 0.850772 | 0.522089 | 0.080980 | -0.212195 | 1 | 1 |
| 191 | 1.459775 | 0.362402 | 1.602060 | 0.094609 | 1.169480 | -0.588678 | 2 | 1 |
| 192 | 1.923340 | 0.915773 | 1.920236 | 1.121678 | 0.547246 | 0.737895 | 2 | 1 |
| 193 | 1.454162 | 0.509817 | 1.761284 | 0.561861 | 1.164134 | -0.911890 | 2 | 1 |
| 194 | 1.480242 | -0.198385 | -0.064509 | -0.994178 | -1.627007 | -0.646308 | 2 | 1 |
| 195 | 1.049366 | -0.397640 | 0.632599 | -0.627315 | -1.290501 | -0.733029 | 2 | 1 |
| 196 | 1.338278 | -0.473726 | 0.747138 | -0.872384 | -1.426898 | -0.885808 | 2 | 1 |
| 197 | 0.405835 | 1.250358 | -0.440807 | 0.810338 | -0.858610 | 1.349151 | 3 | 1 |
| 198 | 0.181164 | 0.930372 | -0.282564 | 0.193104 | -0.467723 | 0.358204 | 3 | 1 |
| 199 | 0.670208 | 1.198906 | -0.303904 | -0.039900 | -0.757903 | 0.813867 | 3 | 1 |
| 200 | -0.765252 | -0.882395 | 0.316745 | 1.160239 | -0.216695 | -0.363017 | 1 | 1 |
| 201 | -0.362519 | -1.407213 | -1.115582 | 0.682467 | 1.234600 | 0.157507 | 3 | 1 |
| 202 | 0.146885 | -1.935391 | 0.147533 | 0.380456 | 2.154532 | 1.489885 | 3 | 1 |
| 203 | 0.828916 | -0.454682 | -0.332837 | 0.712552 | 0.806644 | 0.818245 | 3 | 1 |
| 204 | 0.763529 | -1.226092 | 0.321224 | -0.239370 | -1.883123 | 0.931987 | 3 | 1 |
| 205 | 1.016504 | -0.832857 | -0.443373 | -0.082946 | 0.382222 | 0.809814 | 3 | 1 |
| 206 | 0.227287 | -0.956642 | -0.917348 | -0.459838 | -0.185694 | -0.045102 | 3 | 1 |
| 207 | 0.673549 | -0.883374 | -1.272948 | 0.092401 | 0.071859 | -0.695644 | 3 | 1 |
| 208 | 0.720898 | -1.292898 | -0.751577 | -0.256872 | 0.665717 | 0.628246 | 3 | 1 |
| 209 | -0.792808 | 0.902811 | 0.937273 | 1.790379 | 0.060934 | 0.443300 | 1 | 1 |
| 210 | -1.327416 | 1.434383 | 2.722415 | 1.254539 | 0.971727 | 1.122808 | 1 | 1 |
| 211 | -0.784226 | 0.939410 | 1.388065 | 1.619603 | -0.624808 | 0.083838 | 1 | 1 |
| 212 | -1.373692 | 0.720443 | -0.358383 | 0.586952 | -0.315282 | -0.239589 | 1 | 1 |
| 213 | -1.288536 | 0.664754 | -0.497221 | 0.792405 | -0.824437 | 0.490092 | 1 | 1 |
| 214 | 0.335584 | -0.975748 | -1.771014 | 0.289243 | 2.297665 | 0.370714 | 3 | 1 |
| 215 | 0.882521 | 2.087302 | -1.396329 | -2.436266 | -0.577825 | -1.293233 | 0 | 1 |
216 rows × 8 columns
In [300]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[300]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b82ccc8160>
In [301]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[3]))
Hotel Marrakech¶
ANN¶
In [200]:
X = df_n_ps_std_tc[3]
In [201]:
y = df_n_ps[3]['chosen']
In [202]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [203]:
X_train.shape
Out[203]:
(108, 6)
In [204]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [205]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [206]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [309]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (20, 20), 'learning_rate_init': 0.003, 'max_iter': 2000}, que permiten obtener un Accuracy de 79.63% y un Kappa del 57.62
Tiempo total: 20.07 minutos
In [207]:
grid.best_params_= {'activation': 'tanh', 'hidden_layer_sizes': (20, 20), 'learning_rate_init': 0.003, 'max_iter': 2000}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [208]:
input_tensor = Input(shape = (n0,))
In [209]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [210]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [211]:
model.summary()
Model: "model_10" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_11 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_28 (Dense) (None, 20) 140 _________________________________________________________________ dense_29 (Dense) (None, 20) 420 _________________________________________________________________ dense_30 (Dense) (None, 1) 21 ================================================================= Total params: 581 Trainable params: 581 Non-trainable params: 0 _________________________________________________________________
In [212]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 108 samples, validate on 36 samples Epoch 1/2000 108/108 [==============================] - 0s 2ms/step - loss: 0.6846 - accuracy: 0.5741 - val_loss: 0.7108 - val_accuracy: 0.5833 Epoch 2/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6691 - accuracy: 0.6204 - val_loss: 0.7129 - val_accuracy: 0.5000 Epoch 3/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6606 - accuracy: 0.6481 - val_loss: 0.7103 - val_accuracy: 0.5278 Epoch 4/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6552 - accuracy: 0.6574 - val_loss: 0.7159 - val_accuracy: 0.5000 Epoch 5/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6532 - accuracy: 0.6481 - val_loss: 0.7112 - val_accuracy: 0.5278 Epoch 6/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6503 - accuracy: 0.6389 - val_loss: 0.7059 - val_accuracy: 0.5556 Epoch 7/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6476 - accuracy: 0.6389 - val_loss: 0.6971 - val_accuracy: 0.5833 Epoch 8/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6460 - accuracy: 0.6667 - val_loss: 0.6922 - val_accuracy: 0.5833 Epoch 9/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6462 - accuracy: 0.6481 - val_loss: 0.6955 - val_accuracy: 0.5833 Epoch 10/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6435 - accuracy: 0.6574 - val_loss: 0.6958 - val_accuracy: 0.6111 Epoch 11/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6416 - accuracy: 0.6759 - val_loss: 0.6984 - val_accuracy: 0.6111 Epoch 12/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6403 - accuracy: 0.6667 - val_loss: 0.6955 - val_accuracy: 0.5833 Epoch 13/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6377 - accuracy: 0.6667 - val_loss: 0.6941 - val_accuracy: 0.5833 Epoch 14/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6366 - accuracy: 0.6667 - val_loss: 0.6967 - val_accuracy: 0.5278 Epoch 15/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6362 - accuracy: 0.6574 - val_loss: 0.6968 - val_accuracy: 0.5278 Epoch 16/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6350 - accuracy: 0.6574 - val_loss: 0.7024 - val_accuracy: 0.5278 Epoch 17/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6328 - accuracy: 0.6667 - val_loss: 0.7004 - val_accuracy: 0.5000 Epoch 18/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6312 - accuracy: 0.6667 - val_loss: 0.6960 - val_accuracy: 0.5000 Epoch 19/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6297 - accuracy: 0.6574 - val_loss: 0.6974 - val_accuracy: 0.5000 Epoch 20/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6269 - accuracy: 0.6481 - val_loss: 0.6949 - val_accuracy: 0.5278 Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 21/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6256 - accuracy: 0.6574 - val_loss: 0.6913 - val_accuracy: 0.5278 Epoch 22/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6245 - accuracy: 0.6574 - val_loss: 0.6865 - val_accuracy: 0.5278 Epoch 23/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6237 - accuracy: 0.6574 - val_loss: 0.6851 - val_accuracy: 0.5556 Epoch 24/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6229 - accuracy: 0.6481 - val_loss: 0.6853 - val_accuracy: 0.5556 Epoch 25/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6223 - accuracy: 0.6574 - val_loss: 0.6850 - val_accuracy: 0.5556 Epoch 26/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6208 - accuracy: 0.6574 - val_loss: 0.6852 - val_accuracy: 0.5556 Epoch 27/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6202 - accuracy: 0.6574 - val_loss: 0.6840 - val_accuracy: 0.5556 Epoch 28/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6190 - accuracy: 0.6574 - val_loss: 0.6850 - val_accuracy: 0.5278 Epoch 29/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6182 - accuracy: 0.6667 - val_loss: 0.6840 - val_accuracy: 0.5278 Epoch 30/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6169 - accuracy: 0.6667 - val_loss: 0.6847 - val_accuracy: 0.5278 Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 31/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6157 - accuracy: 0.6667 - val_loss: 0.6870 - val_accuracy: 0.5278 Epoch 32/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6152 - accuracy: 0.6759 - val_loss: 0.6882 - val_accuracy: 0.5278 Epoch 33/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6148 - accuracy: 0.6759 - val_loss: 0.6879 - val_accuracy: 0.5278 Epoch 34/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6141 - accuracy: 0.6759 - val_loss: 0.6886 - val_accuracy: 0.5278 Epoch 35/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6135 - accuracy: 0.6759 - val_loss: 0.6892 - val_accuracy: 0.5278 Epoch 36/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6131 - accuracy: 0.6759 - val_loss: 0.6894 - val_accuracy: 0.5000 Epoch 37/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6125 - accuracy: 0.6759 - val_loss: 0.6904 - val_accuracy: 0.5000 Epoch 38/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6119 - accuracy: 0.6759 - val_loss: 0.6905 - val_accuracy: 0.5000 Epoch 39/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6113 - accuracy: 0.6759 - val_loss: 0.6909 - val_accuracy: 0.5000 Epoch 40/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6107 - accuracy: 0.6759 - val_loss: 0.6911 - val_accuracy: 0.5000 Epoch 00040: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 41/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6102 - accuracy: 0.6759 - val_loss: 0.6915 - val_accuracy: 0.5000 Epoch 42/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6099 - accuracy: 0.6759 - val_loss: 0.6919 - val_accuracy: 0.5000 Epoch 43/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6096 - accuracy: 0.6759 - val_loss: 0.6924 - val_accuracy: 0.5000 Epoch 44/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6095 - accuracy: 0.6759 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 45/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6091 - accuracy: 0.6759 - val_loss: 0.6933 - val_accuracy: 0.5000 Epoch 46/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6088 - accuracy: 0.6759 - val_loss: 0.6934 - val_accuracy: 0.5000 Epoch 47/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6086 - accuracy: 0.6759 - val_loss: 0.6933 - val_accuracy: 0.5000 Epoch 48/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6083 - accuracy: 0.6759 - val_loss: 0.6934 - val_accuracy: 0.5000 Epoch 49/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6081 - accuracy: 0.6759 - val_loss: 0.6942 - val_accuracy: 0.5000 Epoch 50/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6077 - accuracy: 0.6852 - val_loss: 0.6948 - val_accuracy: 0.5000 Epoch 00050: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 51/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6074 - accuracy: 0.6852 - val_loss: 0.6946 - val_accuracy: 0.5000 Epoch 52/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6073 - accuracy: 0.6759 - val_loss: 0.6943 - val_accuracy: 0.5000 Epoch 53/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6072 - accuracy: 0.6759 - val_loss: 0.6941 - val_accuracy: 0.5000 Epoch 54/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6071 - accuracy: 0.6759 - val_loss: 0.6941 - val_accuracy: 0.5000 Epoch 55/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6070 - accuracy: 0.6759 - val_loss: 0.6941 - val_accuracy: 0.5000 Epoch 56/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6068 - accuracy: 0.6759 - val_loss: 0.6939 - val_accuracy: 0.5000 Epoch 57/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6066 - accuracy: 0.6759 - val_loss: 0.6938 - val_accuracy: 0.5000 Epoch 58/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6065 - accuracy: 0.6852 - val_loss: 0.6938 - val_accuracy: 0.5000 Epoch 59/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6063 - accuracy: 0.6852 - val_loss: 0.6936 - val_accuracy: 0.5000 Epoch 60/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6061 - accuracy: 0.6852 - val_loss: 0.6933 - val_accuracy: 0.5000 Epoch 00060: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 61/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6061 - accuracy: 0.6852 - val_loss: 0.6932 - val_accuracy: 0.5000 Epoch 62/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6060 - accuracy: 0.6944 - val_loss: 0.6931 - val_accuracy: 0.5000 Epoch 63/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6059 - accuracy: 0.6944 - val_loss: 0.6930 - val_accuracy: 0.5000 Epoch 64/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6058 - accuracy: 0.6944 - val_loss: 0.6930 - val_accuracy: 0.5000 Epoch 65/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6057 - accuracy: 0.6944 - val_loss: 0.6929 - val_accuracy: 0.5000 Epoch 66/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6056 - accuracy: 0.6944 - val_loss: 0.6928 - val_accuracy: 0.5000 Epoch 67/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6056 - accuracy: 0.6944 - val_loss: 0.6925 - val_accuracy: 0.5000 Epoch 68/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6055 - accuracy: 0.6944 - val_loss: 0.6924 - val_accuracy: 0.5000 Epoch 69/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6054 - accuracy: 0.6944 - val_loss: 0.6925 - val_accuracy: 0.5000 Epoch 70/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6053 - accuracy: 0.6944 - val_loss: 0.6924 - val_accuracy: 0.5000 Epoch 00070: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 71/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6053 - accuracy: 0.6944 - val_loss: 0.6925 - val_accuracy: 0.5000 Epoch 72/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6052 - accuracy: 0.6944 - val_loss: 0.6925 - val_accuracy: 0.5000 Epoch 73/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6052 - accuracy: 0.6944 - val_loss: 0.6924 - val_accuracy: 0.5000 Epoch 74/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6051 - accuracy: 0.6944 - val_loss: 0.6923 - val_accuracy: 0.5000 Epoch 75/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6051 - accuracy: 0.6944 - val_loss: 0.6922 - val_accuracy: 0.5000 Epoch 76/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6051 - accuracy: 0.6944 - val_loss: 0.6922 - val_accuracy: 0.5000 Epoch 77/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6050 - accuracy: 0.6944 - val_loss: 0.6922 - val_accuracy: 0.5000 Epoch 78/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6050 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 79/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6050 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 80/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6049 - accuracy: 0.6944 - val_loss: 0.6922 - val_accuracy: 0.5000 Epoch 00080: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 81/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6049 - accuracy: 0.6944 - val_loss: 0.6922 - val_accuracy: 0.5000 Epoch 82/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6049 - accuracy: 0.6944 - val_loss: 0.6922 - val_accuracy: 0.5000 Epoch 83/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6048 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 84/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6048 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 85/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6048 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 86/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6048 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 87/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6048 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 88/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6047 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 89/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6047 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 90/2000 108/108 [==============================] - 0s 74us/step - loss: 0.6047 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 00090: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 91/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6047 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 92/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6047 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 93/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6047 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 94/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6046 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 95/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6046 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 96/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6046 - accuracy: 0.6944 - val_loss: 0.6921 - val_accuracy: 0.5000 Epoch 97/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6046 - accuracy: 0.6944 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 98/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6046 - accuracy: 0.6944 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 99/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6046 - accuracy: 0.6944 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 100/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6046 - accuracy: 0.6944 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00100: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 101/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6046 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 102/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6046 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 103/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6046 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 104/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6046 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 105/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6046 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 106/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6046 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 107/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6046 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 108/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 109/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 110/2000 108/108 [==============================] - 0s 83us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00110: ReduceLROnPlateau reducing learning rate to 2.9296875254658516e-06. Epoch 111/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 112/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 113/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 114/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 115/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 116/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 117/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 118/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 119/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 120/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00120: ReduceLROnPlateau reducing learning rate to 1.4648437627329258e-06. Epoch 121/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 122/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 123/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 124/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 125/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 126/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 127/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 128/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 129/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 130/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00130: ReduceLROnPlateau reducing learning rate to 7.324218813664629e-07. Epoch 131/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 132/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 133/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 134/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 135/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 136/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 137/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 138/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 139/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 140/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00140: ReduceLROnPlateau reducing learning rate to 3.6621094068323146e-07. Epoch 141/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 142/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 143/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 144/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 145/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 146/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 147/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 148/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 149/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 150/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00150: ReduceLROnPlateau reducing learning rate to 1.8310547034161573e-07. Epoch 151/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 152/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 153/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 154/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 155/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 156/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 157/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 158/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 159/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 160/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00160: ReduceLROnPlateau reducing learning rate to 9.155273517080786e-08. Epoch 161/2000 108/108 [==============================] - 0s 222us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 162/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 163/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 164/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 165/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 166/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 167/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 168/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 169/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 170/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00170: ReduceLROnPlateau reducing learning rate to 4.577636758540393e-08. Epoch 171/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 172/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 173/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 174/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 175/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 176/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 177/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 178/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 179/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 180/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00180: ReduceLROnPlateau reducing learning rate to 2.2888183792701966e-08. Epoch 181/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 182/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 183/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 184/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 185/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 186/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 187/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 188/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 189/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 190/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00190: ReduceLROnPlateau reducing learning rate to 1.1444091896350983e-08. Epoch 191/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 192/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 193/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 194/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 195/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 196/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 197/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 198/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 199/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 200/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00200: ReduceLROnPlateau reducing learning rate to 5.7220459481754915e-09. Epoch 201/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 202/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 203/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 204/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 205/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 206/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 207/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 208/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 209/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 210/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00210: ReduceLROnPlateau reducing learning rate to 2.8610229740877458e-09. Epoch 211/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 212/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 213/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 214/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 215/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 216/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 217/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 218/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 219/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 220/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00220: ReduceLROnPlateau reducing learning rate to 1.4305114870438729e-09. Epoch 221/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 222/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 223/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 224/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 225/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 226/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 227/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 228/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 229/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 230/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00230: ReduceLROnPlateau reducing learning rate to 7.152557435219364e-10. Epoch 231/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 232/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 233/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 234/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 235/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 236/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 237/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 238/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 239/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 240/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00240: ReduceLROnPlateau reducing learning rate to 3.576278717609682e-10. Epoch 241/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 242/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 243/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 244/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 245/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 246/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 247/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 248/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 249/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 250/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00250: ReduceLROnPlateau reducing learning rate to 1.788139358804841e-10. Epoch 251/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 252/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 253/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 254/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 255/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 256/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 257/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 258/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 259/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 260/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00260: ReduceLROnPlateau reducing learning rate to 8.940696794024205e-11. Epoch 261/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 262/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 263/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 264/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 265/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 266/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 267/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 268/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 269/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 270/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00270: ReduceLROnPlateau reducing learning rate to 4.470348397012103e-11. Epoch 271/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 272/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 273/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 274/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 275/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 276/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 277/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 278/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 279/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 280/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00280: ReduceLROnPlateau reducing learning rate to 2.2351741985060514e-11. Epoch 281/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 282/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 283/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 284/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 285/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 286/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 287/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 288/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 289/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 290/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00290: ReduceLROnPlateau reducing learning rate to 1.1175870992530257e-11. Epoch 291/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 292/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 293/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 294/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 295/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 296/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 297/2000 108/108 [==============================] - 0s 194us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 298/2000 108/108 [==============================] - ETA: 0s - loss: 0.5656 - accuracy: 0.75 - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 299/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 300/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00300: ReduceLROnPlateau reducing learning rate to 5.5879354962651284e-12. Epoch 301/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 302/2000 108/108 [==============================] - 0s 204us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 303/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 304/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 305/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 306/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 307/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 308/2000 108/108 [==============================] - 0s 194us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 309/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 310/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00310: ReduceLROnPlateau reducing learning rate to 2.7939677481325642e-12. Epoch 311/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 312/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 313/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 314/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 315/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 316/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 317/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 318/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 319/2000 108/108 [==============================] - 0s 370us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 320/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00320: ReduceLROnPlateau reducing learning rate to 1.3969838740662821e-12. Epoch 321/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 322/2000 108/108 [==============================] - 0s 287us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 323/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 324/2000 108/108 [==============================] - 0s 268us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 325/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 326/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 327/2000 108/108 [==============================] - 0s 231us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 328/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 329/2000 108/108 [==============================] - 0s 231us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 330/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00330: ReduceLROnPlateau reducing learning rate to 6.984919370331411e-13. Epoch 331/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 332/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 333/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 334/2000 108/108 [==============================] - 0s 222us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 335/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 336/2000 108/108 [==============================] - 0s 213us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 337/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 338/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 339/2000 108/108 [==============================] - 0s 287us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 340/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00340: ReduceLROnPlateau reducing learning rate to 3.4924596851657053e-13. Epoch 341/2000 108/108 [==============================] - 0s 231us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 342/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 343/2000 108/108 [==============================] - 0s 315us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 344/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 345/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 346/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 347/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 348/2000 108/108 [==============================] - 0s 204us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 349/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 350/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00350: ReduceLROnPlateau reducing learning rate to 1.7462298425828526e-13. Epoch 351/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 352/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 353/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 354/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 355/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 356/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 357/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 358/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 359/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 360/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00360: ReduceLROnPlateau reducing learning rate to 8.731149212914263e-14. Epoch 361/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 362/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 363/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 364/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 365/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 366/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 367/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 368/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 369/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 370/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00370: ReduceLROnPlateau reducing learning rate to 4.3655746064571316e-14. Epoch 371/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 372/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 373/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 374/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 375/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 376/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 377/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 378/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 379/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 380/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00380: ReduceLROnPlateau reducing learning rate to 2.1827873032285658e-14. Epoch 381/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 382/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 383/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 384/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 385/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 386/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 387/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 388/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 389/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 390/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00390: ReduceLROnPlateau reducing learning rate to 1.0913936516142829e-14. Epoch 391/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 392/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 393/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 394/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 395/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 396/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 397/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 398/2000 108/108 [==============================] - 0s 185us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 399/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 400/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00400: ReduceLROnPlateau reducing learning rate to 5.4569682580714145e-15. Epoch 401/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 402/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 403/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 404/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 405/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 406/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 407/2000 108/108 [==============================] - 0s 185us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 408/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 409/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 410/2000 108/108 [==============================] - 0s 204us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00410: ReduceLROnPlateau reducing learning rate to 2.7284841290357072e-15. Epoch 411/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 412/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 413/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 414/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 415/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 416/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 417/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 418/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 419/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 420/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00420: ReduceLROnPlateau reducing learning rate to 1.3642420645178536e-15. Epoch 421/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 422/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 423/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 424/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 425/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 426/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 427/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 428/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 429/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 430/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00430: ReduceLROnPlateau reducing learning rate to 6.821210322589268e-16. Epoch 431/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 432/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 433/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 434/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 435/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 436/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 437/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 438/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 439/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 440/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00440: ReduceLROnPlateau reducing learning rate to 3.410605161294634e-16. Epoch 441/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 442/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 443/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 444/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 445/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 446/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 447/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 448/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 449/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 450/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00450: ReduceLROnPlateau reducing learning rate to 1.705302580647317e-16. Epoch 451/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 452/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 453/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 454/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 455/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 456/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 457/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 458/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 459/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 460/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00460: ReduceLROnPlateau reducing learning rate to 8.526512903236585e-17. Epoch 461/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 462/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 463/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 464/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 465/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 466/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 467/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 468/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 469/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 470/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00470: ReduceLROnPlateau reducing learning rate to 4.2632564516182926e-17. Epoch 471/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 472/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 473/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 474/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 475/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 476/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 477/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 478/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 479/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 480/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00480: ReduceLROnPlateau reducing learning rate to 2.1316282258091463e-17. Epoch 481/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 482/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 483/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 484/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 485/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 486/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 487/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 488/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 489/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 490/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00490: ReduceLROnPlateau reducing learning rate to 1.0658141129045731e-17. Epoch 491/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 492/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 493/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 494/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 495/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 496/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 497/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 498/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 499/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 500/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00500: ReduceLROnPlateau reducing learning rate to 5.329070564522866e-18. Epoch 501/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 502/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 503/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 504/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 505/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 506/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 507/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 508/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 509/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 510/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00510: ReduceLROnPlateau reducing learning rate to 2.664535282261433e-18. Epoch 511/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 512/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 513/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 514/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 515/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 516/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 517/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 518/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 519/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 520/2000 108/108 [==============================] - ETA: 0s - loss: 0.6291 - accuracy: 0.68 - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00520: ReduceLROnPlateau reducing learning rate to 1.3322676411307164e-18. Epoch 521/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 522/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 523/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 524/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 525/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 526/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 527/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 528/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 529/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 530/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00530: ReduceLROnPlateau reducing learning rate to 6.661338205653582e-19. Epoch 531/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 532/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 533/2000 108/108 [==============================] - 0s 204us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 534/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 535/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 536/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 537/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 538/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 539/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 540/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00540: ReduceLROnPlateau reducing learning rate to 3.330669102826791e-19. Epoch 541/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 542/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 543/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 544/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 545/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 546/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 547/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 548/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 549/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 550/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00550: ReduceLROnPlateau reducing learning rate to 1.6653345514133955e-19. Epoch 551/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 552/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 553/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 554/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 555/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 556/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 557/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 558/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 559/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 560/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00560: ReduceLROnPlateau reducing learning rate to 8.326672757066978e-20. Epoch 561/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 562/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 563/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 564/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 565/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 566/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 567/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 568/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 569/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 570/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00570: ReduceLROnPlateau reducing learning rate to 4.163336378533489e-20. Epoch 571/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 572/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 573/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 574/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 575/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 576/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 577/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 578/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 579/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 580/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00580: ReduceLROnPlateau reducing learning rate to 2.0816681892667444e-20. Epoch 581/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 582/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 583/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 584/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 585/2000 108/108 [==============================] - ETA: 0s - loss: 0.5617 - accuracy: 0.75 - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 586/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 587/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 588/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 589/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 590/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00590: ReduceLROnPlateau reducing learning rate to 1.0408340946333722e-20. Epoch 591/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 592/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 593/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 594/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 595/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 596/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 597/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 598/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 599/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 600/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00600: ReduceLROnPlateau reducing learning rate to 5.204170473166861e-21. Epoch 601/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 602/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 603/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 604/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 605/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 606/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 607/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 608/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 609/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 610/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00610: ReduceLROnPlateau reducing learning rate to 2.6020852365834305e-21. Epoch 611/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 612/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 613/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 614/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 615/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 616/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 617/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 618/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 619/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 620/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00620: ReduceLROnPlateau reducing learning rate to 1.3010426182917153e-21. Epoch 621/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 622/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 623/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 624/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 625/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 626/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 627/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 628/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 629/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 630/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00630: ReduceLROnPlateau reducing learning rate to 6.505213091458576e-22. Epoch 631/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 632/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 633/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 634/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 635/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 636/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 637/2000 108/108 [==============================] - ETA: 0s - loss: 0.6880 - accuracy: 0.56 - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 638/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 639/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 640/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00640: ReduceLROnPlateau reducing learning rate to 3.252606545729288e-22. Epoch 641/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 642/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 643/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 644/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 645/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 646/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 647/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 648/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 649/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 650/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00650: ReduceLROnPlateau reducing learning rate to 1.626303272864644e-22. Epoch 651/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 652/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 653/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 654/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 655/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 656/2000 108/108 [==============================] - 0s 194us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 657/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 658/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 659/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 660/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00660: ReduceLROnPlateau reducing learning rate to 8.13151636432322e-23. Epoch 661/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 662/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 663/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 664/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 665/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 666/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 667/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 668/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 669/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 670/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00670: ReduceLROnPlateau reducing learning rate to 4.06575818216161e-23. Epoch 671/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 672/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 673/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 674/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 675/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 676/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 677/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 678/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 679/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 680/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00680: ReduceLROnPlateau reducing learning rate to 2.032879091080805e-23. Epoch 681/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 682/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 683/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 684/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 685/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 686/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 687/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 688/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 689/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 690/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00690: ReduceLROnPlateau reducing learning rate to 1.0164395455404025e-23. Epoch 691/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 692/2000 108/108 [==============================] - 0s 185us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 693/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 694/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 695/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 696/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 697/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 698/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 699/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 700/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00700: ReduceLROnPlateau reducing learning rate to 5.082197727702013e-24. Epoch 701/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 702/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 703/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 704/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 705/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 706/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 707/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 708/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 709/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 710/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00710: ReduceLROnPlateau reducing learning rate to 2.5410988638510064e-24. Epoch 711/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 712/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 713/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 714/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 715/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 716/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 717/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 718/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 719/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 720/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00720: ReduceLROnPlateau reducing learning rate to 1.2705494319255032e-24. Epoch 721/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 722/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 723/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 724/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 725/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 726/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 727/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 728/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 729/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 730/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00730: ReduceLROnPlateau reducing learning rate to 6.352747159627516e-25. Epoch 731/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 732/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 733/2000 108/108 [==============================] - 0s 213us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 734/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 735/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 736/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 737/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 738/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 739/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 740/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00740: ReduceLROnPlateau reducing learning rate to 3.176373579813758e-25. Epoch 741/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 742/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 743/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 744/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 745/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 746/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 747/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 748/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 749/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 750/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00750: ReduceLROnPlateau reducing learning rate to 1.588186789906879e-25. Epoch 751/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 752/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 753/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 754/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 755/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 756/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 757/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 758/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 759/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 760/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00760: ReduceLROnPlateau reducing learning rate to 7.940933949534395e-26. Epoch 761/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 762/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 763/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 764/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 765/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 766/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 767/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 768/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 769/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 770/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00770: ReduceLROnPlateau reducing learning rate to 3.9704669747671974e-26. Epoch 771/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 772/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 773/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 774/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 775/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 776/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 777/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 778/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 779/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 780/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00780: ReduceLROnPlateau reducing learning rate to 1.9852334873835987e-26. Epoch 781/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 782/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 783/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 784/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 785/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 786/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 787/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 788/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 789/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 790/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00790: ReduceLROnPlateau reducing learning rate to 9.926167436917994e-27. Epoch 791/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 792/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 793/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 794/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 795/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 796/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 797/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 798/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 799/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 800/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00800: ReduceLROnPlateau reducing learning rate to 4.963083718458997e-27. Epoch 801/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 802/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 803/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 804/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 805/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 806/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 807/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 808/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 809/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 810/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00810: ReduceLROnPlateau reducing learning rate to 2.4815418592294984e-27. Epoch 811/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 812/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 813/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 814/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 815/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 816/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 817/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 818/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 819/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 820/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00820: ReduceLROnPlateau reducing learning rate to 1.2407709296147492e-27. Epoch 821/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 822/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 823/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 824/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 825/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 826/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 827/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 828/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 829/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 830/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00830: ReduceLROnPlateau reducing learning rate to 6.203854648073746e-28. Epoch 831/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 832/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 833/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 834/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 835/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 836/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 837/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 838/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 839/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 840/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00840: ReduceLROnPlateau reducing learning rate to 3.101927324036873e-28. Epoch 841/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 842/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 843/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 844/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 845/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 846/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 847/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 848/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 849/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 850/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00850: ReduceLROnPlateau reducing learning rate to 1.5509636620184365e-28. Epoch 851/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 852/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 853/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 854/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 855/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 856/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 857/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 858/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 859/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 860/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00860: ReduceLROnPlateau reducing learning rate to 7.754818310092183e-29. Epoch 861/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 862/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 863/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 864/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 865/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 866/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 867/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 868/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 869/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 870/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00870: ReduceLROnPlateau reducing learning rate to 3.877409155046091e-29. Epoch 871/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 872/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 873/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 874/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 875/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 876/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 877/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 878/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 879/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 880/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00880: ReduceLROnPlateau reducing learning rate to 1.9387045775230456e-29. Epoch 881/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 882/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 883/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 884/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 885/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 886/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 887/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 888/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 889/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 890/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00890: ReduceLROnPlateau reducing learning rate to 9.693522887615228e-30. Epoch 891/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 892/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 893/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 894/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 895/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 896/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 897/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 898/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 899/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 900/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00900: ReduceLROnPlateau reducing learning rate to 4.846761443807614e-30. Epoch 901/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 902/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 903/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 904/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 905/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 906/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 907/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 908/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 909/2000 108/108 [==============================] - 0s 185us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 910/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00910: ReduceLROnPlateau reducing learning rate to 2.423380721903807e-30. Epoch 911/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 912/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 913/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 914/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 915/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 916/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 917/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 918/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 919/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 920/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00920: ReduceLROnPlateau reducing learning rate to 1.2116903609519035e-30. Epoch 921/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 922/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 923/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 924/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 925/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 926/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 927/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 928/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 929/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 930/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00930: ReduceLROnPlateau reducing learning rate to 6.058451804759518e-31. Epoch 931/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 932/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 933/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 934/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 935/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 936/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 937/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 938/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 939/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 940/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00940: ReduceLROnPlateau reducing learning rate to 3.029225902379759e-31. Epoch 941/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 942/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 943/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 944/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 945/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 946/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 947/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 948/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 949/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 950/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00950: ReduceLROnPlateau reducing learning rate to 1.5146129511898794e-31. Epoch 951/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 952/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 953/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 954/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 955/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 956/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 957/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 958/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 959/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 960/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00960: ReduceLROnPlateau reducing learning rate to 7.573064755949397e-32. Epoch 961/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 962/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 963/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 964/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 965/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 966/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 967/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 968/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 969/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 970/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00970: ReduceLROnPlateau reducing learning rate to 3.7865323779746985e-32. Epoch 971/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 972/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 973/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 974/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 975/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 976/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 977/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 978/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 979/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 980/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00980: ReduceLROnPlateau reducing learning rate to 1.8932661889873492e-32. Epoch 981/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 982/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 983/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 984/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 985/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 986/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 987/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 988/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 989/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 990/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 00990: ReduceLROnPlateau reducing learning rate to 9.466330944936746e-33. Epoch 991/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 992/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 993/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 994/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 995/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 996/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 997/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 998/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 999/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1000/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01000: ReduceLROnPlateau reducing learning rate to 4.733165472468373e-33. Epoch 1001/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1002/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1003/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1004/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1005/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1006/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1007/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1008/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1009/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1010/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01010: ReduceLROnPlateau reducing learning rate to 2.3665827362341866e-33. Epoch 1011/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1012/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1013/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1014/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1015/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1016/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1017/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1018/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1019/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1020/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01020: ReduceLROnPlateau reducing learning rate to 1.1832913681170933e-33. Epoch 1021/2000 108/108 [==============================] - ETA: 0s - loss: 0.5831 - accuracy: 0.68 - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1022/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1023/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1024/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1025/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1026/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1027/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1028/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1029/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1030/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01030: ReduceLROnPlateau reducing learning rate to 5.916456840585466e-34. Epoch 1031/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1032/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1033/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1034/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1035/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1036/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1037/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1038/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1039/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1040/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01040: ReduceLROnPlateau reducing learning rate to 2.958228420292733e-34. Epoch 1041/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1042/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1043/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1044/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1045/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1046/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1047/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1048/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1049/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1050/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01050: ReduceLROnPlateau reducing learning rate to 1.4791142101463666e-34. Epoch 1051/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1052/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1053/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1054/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1055/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1056/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1057/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1058/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1059/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1060/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01060: ReduceLROnPlateau reducing learning rate to 7.395571050731833e-35. Epoch 1061/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1062/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1063/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1064/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1065/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1066/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1067/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1068/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1069/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1070/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01070: ReduceLROnPlateau reducing learning rate to 3.6977855253659165e-35. Epoch 1071/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1072/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1073/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1074/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1075/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1076/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1077/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1078/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1079/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1080/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01080: ReduceLROnPlateau reducing learning rate to 1.8488927626829582e-35. Epoch 1081/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1082/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1083/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1084/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1085/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1086/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1087/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1088/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1089/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1090/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01090: ReduceLROnPlateau reducing learning rate to 9.244463813414791e-36. Epoch 1091/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1092/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1093/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1094/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1095/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1096/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1097/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1098/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1099/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1100/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01100: ReduceLROnPlateau reducing learning rate to 4.6222319067073956e-36. Epoch 1101/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1102/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1103/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1104/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1105/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1106/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1107/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1108/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1109/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1110/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01110: ReduceLROnPlateau reducing learning rate to 2.3111159533536978e-36. Epoch 1111/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1112/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1113/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1114/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1115/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1116/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1117/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1118/2000 108/108 [==============================] - ETA: 0s - loss: 0.5864 - accuracy: 0.68 - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1119/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1120/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01120: ReduceLROnPlateau reducing learning rate to 1.1555579766768489e-36. Epoch 1121/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1122/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1123/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1124/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1125/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1126/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1127/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1128/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1129/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1130/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01130: ReduceLROnPlateau reducing learning rate to 5.7777898833842445e-37. Epoch 1131/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1132/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1133/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1134/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1135/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1136/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1137/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1138/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1139/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1140/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01140: ReduceLROnPlateau reducing learning rate to 2.8888949416921223e-37. Epoch 1141/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1142/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1143/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1144/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1145/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1146/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1147/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1148/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1149/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1150/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01150: ReduceLROnPlateau reducing learning rate to 1.4444474708460611e-37. Epoch 1151/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1152/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1153/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1154/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1155/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1156/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1157/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1158/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1159/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1160/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01160: ReduceLROnPlateau reducing learning rate to 7.222237354230306e-38. Epoch 1161/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1162/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1163/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1164/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1165/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1166/2000 108/108 [==============================] - 0s 250us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1167/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1168/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1169/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1170/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01170: ReduceLROnPlateau reducing learning rate to 3.611118677115153e-38. Epoch 1171/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1172/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1173/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1174/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1175/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1176/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1177/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1178/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1179/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1180/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01180: ReduceLROnPlateau reducing learning rate to 1.8055593385575764e-38. Epoch 1181/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1182/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1183/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1184/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1185/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1186/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1187/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1188/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1189/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1190/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01190: ReduceLROnPlateau reducing learning rate to 9.027796692787882e-39. Epoch 1191/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1192/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1193/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1194/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1195/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1196/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1197/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1198/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1199/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1200/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01200: ReduceLROnPlateau reducing learning rate to 4.513898346393941e-39. Epoch 1201/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1202/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1203/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1204/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1205/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1206/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1207/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1208/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1209/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1210/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01210: ReduceLROnPlateau reducing learning rate to 2.2569495235215866e-39. Epoch 1211/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1212/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1213/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1214/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1215/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1216/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1217/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1218/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1219/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1220/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01220: ReduceLROnPlateau reducing learning rate to 1.1284747617607933e-39. Epoch 1221/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1222/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1223/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1224/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1225/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1226/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1227/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1228/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1229/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1230/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01230: ReduceLROnPlateau reducing learning rate to 5.642370305557806e-40. Epoch 1231/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1232/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1233/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1234/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1235/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1236/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1237/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1238/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1239/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1240/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01240: ReduceLROnPlateau reducing learning rate to 2.821185152778903e-40. Epoch 1241/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1242/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1243/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1244/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1245/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1246/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1247/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1248/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1249/2000 108/108 [==============================] - 0s 185us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1250/2000 108/108 [==============================] - 0s 185us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01250: ReduceLROnPlateau reducing learning rate to 1.4105890731432906e-40. Epoch 1251/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1252/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1253/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1254/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1255/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1256/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1257/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1258/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1259/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1260/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01260: ReduceLROnPlateau reducing learning rate to 7.052945365716453e-41. Epoch 1261/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1262/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1263/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1264/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1265/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1266/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1267/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1268/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1269/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1270/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01270: ReduceLROnPlateau reducing learning rate to 3.5265077153198346e-41. Epoch 1271/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1272/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1273/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1274/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1275/2000 108/108 [==============================] - 0s 129us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1276/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1277/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1278/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1279/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1280/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01280: ReduceLROnPlateau reducing learning rate to 1.7632538576599173e-41. Epoch 1281/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1282/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1283/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1284/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1285/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1286/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1287/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1288/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1289/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1290/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01290: ReduceLROnPlateau reducing learning rate to 8.816269288299587e-42. Epoch 1291/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1292/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1293/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1294/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1295/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1296/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1297/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1298/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1299/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1300/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01300: ReduceLROnPlateau reducing learning rate to 4.4084849687658745e-42. Epoch 1301/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1302/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1303/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1304/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1305/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1306/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1307/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1308/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1309/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1310/2000 108/108 [==============================] - ETA: 0s - loss: 0.6582 - accuracy: 0.56 - 0s 194us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01310: ReduceLROnPlateau reducing learning rate to 2.2042424843829373e-42. Epoch 1311/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1312/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1313/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1314/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1315/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1316/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1317/2000 108/108 [==============================] - ETA: 0s - loss: 0.6126 - accuracy: 0.68 - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1318/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1319/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1320/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01320: ReduceLROnPlateau reducing learning rate to 1.1021212421914686e-42. Epoch 1321/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1322/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1323/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1324/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1325/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1326/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1327/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1328/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1329/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1330/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01330: ReduceLROnPlateau reducing learning rate to 5.507102964796531e-43. Epoch 1331/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1332/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1333/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1334/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1335/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1336/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1337/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1338/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1339/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1340/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01340: ReduceLROnPlateau reducing learning rate to 2.7535514823982655e-43. Epoch 1341/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1342/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1343/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1344/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1345/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1346/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1347/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1348/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1349/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1350/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01350: ReduceLROnPlateau reducing learning rate to 1.3732724950383207e-43. Epoch 1351/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1352/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1353/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1354/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1355/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1356/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1357/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1358/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1359/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1360/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01360: ReduceLROnPlateau reducing learning rate to 6.866362475191604e-44. Epoch 1361/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1362/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1363/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1364/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1365/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1366/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1367/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1368/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1369/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1370/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01370: ReduceLROnPlateau reducing learning rate to 3.433181237595802e-44. Epoch 1371/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1372/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1373/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1374/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1375/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1376/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1377/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1378/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1379/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1380/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01380: ReduceLROnPlateau reducing learning rate to 1.6815581571897805e-44. Epoch 1381/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1382/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1383/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1384/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1385/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1386/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1387/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1388/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1389/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1390/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01390: ReduceLROnPlateau reducing learning rate to 8.407790785948902e-45. Epoch 1391/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1392/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1393/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1394/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1395/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1396/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1397/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1398/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1399/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1400/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01400: ReduceLROnPlateau reducing learning rate to 4.203895392974451e-45. Epoch 1401/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1402/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1403/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1404/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1405/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1406/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1407/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1408/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1409/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1410/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01410: ReduceLROnPlateau reducing learning rate to 2.1019476964872256e-45. Epoch 1411/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1412/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1413/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1414/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1415/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1416/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1417/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1418/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1419/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1420/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01420: ReduceLROnPlateau reducing learning rate to 1.401298464324817e-45. Epoch 1421/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1422/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1423/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1424/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1425/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1426/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1427/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1428/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1429/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1430/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 01430: ReduceLROnPlateau reducing learning rate to 7.006492321624085e-46. Epoch 1431/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1432/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1433/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1434/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1435/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1436/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1437/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1438/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1439/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1440/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1441/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1442/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1443/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1444/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1445/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1446/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1447/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1448/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1449/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1450/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1451/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1452/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1453/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1454/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1455/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1456/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1457/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1458/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1459/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1460/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1461/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1462/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1463/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1464/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1465/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1466/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1467/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1468/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1469/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1470/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1471/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1472/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1473/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1474/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1475/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1476/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1477/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1478/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1479/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1480/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1481/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1482/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1483/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1484/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1485/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1486/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1487/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1488/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1489/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1490/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1491/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1492/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1493/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1494/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1495/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1496/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1497/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1498/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1499/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1500/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1501/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1502/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1503/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1504/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1505/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1506/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1507/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1508/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1509/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1510/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1511/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1512/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1513/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1514/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1515/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1516/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1517/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1518/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1519/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1520/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1521/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1522/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1523/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1524/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1525/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1526/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1527/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1528/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1529/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1530/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1531/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1532/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1533/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1534/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1535/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1536/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1537/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1538/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1539/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1540/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1541/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1542/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1543/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1544/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1545/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1546/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1547/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1548/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1549/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1550/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1551/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1552/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1553/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1554/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1555/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1556/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1557/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1558/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1559/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1560/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1561/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1562/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1563/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1564/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1565/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1566/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1567/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1568/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1569/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1570/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1571/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1572/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1573/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1574/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1575/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1576/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1577/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1578/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1579/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1580/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1581/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1582/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1583/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1584/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1585/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1586/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1587/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1588/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1589/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1590/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1591/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1592/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1593/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1594/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1595/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1596/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1597/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1598/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1599/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1600/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1601/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1602/2000 108/108 [==============================] - ETA: 0s - loss: 0.5878 - accuracy: 0.71 - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1603/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1604/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1605/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1606/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1607/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1608/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1609/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1610/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1611/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1612/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1613/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1614/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1615/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1616/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1617/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1618/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1619/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1620/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1621/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1622/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1623/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1624/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1625/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1626/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1627/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1628/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1629/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1630/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1631/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1632/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1633/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1634/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1635/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1636/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1637/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1638/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1639/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1640/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1641/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1642/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1643/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1644/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1645/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1646/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1647/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1648/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1649/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1650/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1651/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1652/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1653/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1654/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1655/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1656/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1657/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1658/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1659/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1660/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1661/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1662/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1663/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1664/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1665/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1666/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1667/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1668/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1669/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1670/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1671/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1672/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1673/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1674/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1675/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1676/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1677/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1678/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1679/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1680/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1681/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1682/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1683/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1684/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1685/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1686/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1687/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1688/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1689/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1690/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1691/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1692/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1693/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1694/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1695/2000 108/108 [==============================] - ETA: 0s - loss: 0.5554 - accuracy: 0.75 - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1696/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1697/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1698/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1699/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1700/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1701/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1702/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1703/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1704/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1705/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1706/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1707/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1708/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1709/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1710/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1711/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1712/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1713/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1714/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1715/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1716/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1717/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1718/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1719/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1720/2000 108/108 [==============================] - ETA: 0s - loss: 0.6044 - accuracy: 0.71 - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1721/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1722/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1723/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1724/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1725/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1726/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1727/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1728/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1729/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1730/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1731/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1732/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1733/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1734/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1735/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1736/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1737/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1738/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1739/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1740/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1741/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1742/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1743/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1744/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1745/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1746/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1747/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1748/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1749/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1750/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1751/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1752/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1753/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1754/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1755/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1756/2000 108/108 [==============================] - 0s 185us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1757/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1758/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1759/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1760/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1761/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1762/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1763/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1764/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1765/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1766/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1767/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1768/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1769/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1770/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1771/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1772/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1773/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1774/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1775/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1776/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1777/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1778/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1779/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1780/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1781/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1782/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1783/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1784/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1785/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1786/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1787/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1788/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1789/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1790/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1791/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1792/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1793/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1794/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1795/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1796/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1797/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1798/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1799/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1800/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1801/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1802/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1803/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1804/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1805/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1806/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1807/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1808/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1809/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1810/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1811/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1812/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1813/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1814/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1815/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1816/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1817/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1818/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1819/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1820/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1821/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1822/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1823/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1824/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1825/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1826/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1827/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1828/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1829/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1830/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1831/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1832/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1833/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1834/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1835/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1836/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1837/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1838/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1839/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1840/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1841/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1842/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1843/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1844/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1845/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1846/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1847/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1848/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1849/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1850/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1851/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1852/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1853/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1854/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1855/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1856/2000 108/108 [==============================] - ETA: 0s - loss: 0.5179 - accuracy: 0.84 - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1857/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1858/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1859/2000 108/108 [==============================] - 0s 185us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1860/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1861/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1862/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1863/2000 108/108 [==============================] - 0s 176us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1864/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1865/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1866/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1867/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1868/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1869/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1870/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1871/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1872/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1873/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1874/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1875/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1876/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1877/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1878/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1879/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1880/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1881/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1882/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1883/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1884/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1885/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1886/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1887/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1888/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1889/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1890/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1891/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1892/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1893/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1894/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1895/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1896/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1897/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1898/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1899/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1900/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1901/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1902/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1903/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1904/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1905/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1906/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1907/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1908/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1909/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1910/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1911/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1912/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1913/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1914/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1915/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1916/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1917/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1918/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1919/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1920/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1921/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1922/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1923/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1924/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1925/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1926/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1927/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1928/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1929/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1930/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1931/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1932/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1933/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1934/2000 108/108 [==============================] - 0s 167us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1935/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1936/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1937/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1938/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1939/2000 108/108 [==============================] - 0s 93us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1940/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1941/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1942/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1943/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1944/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1945/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1946/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1947/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1948/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1949/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1950/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1951/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1952/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1953/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1954/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1955/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1956/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1957/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1958/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1959/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1960/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1961/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1962/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1963/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1964/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1965/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1966/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1967/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1968/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1969/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1970/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1971/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1972/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1973/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1974/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1975/2000 108/108 [==============================] - ETA: 0s - loss: 0.6422 - accuracy: 0.68 - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1976/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1977/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1978/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1979/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1980/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1981/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1982/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1983/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1984/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1985/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1986/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1987/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1988/2000 108/108 [==============================] - 0s 102us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1989/2000 108/108 [==============================] - 0s 111us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1990/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1991/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1992/2000 108/108 [==============================] - 0s 120us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1993/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1994/2000 108/108 [==============================] - 0s 157us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1995/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1996/2000 108/108 [==============================] - 0s 148us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1997/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1998/2000 108/108 [==============================] - 0s 139us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 1999/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000 Epoch 2000/2000 108/108 [==============================] - 0s 130us/step - loss: 0.6045 - accuracy: 0.6852 - val_loss: 0.6920 - val_accuracy: 0.5000
In [213]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 2000)
In [214]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
36/36 [==============================] - 0s 111us/step test loss: 0.6919848455323113, test accuracy: 0.5
In [215]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.528428093645485
In [216]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: -0.04854368932038833 [[13 10] [ 8 5]]
KMeans¶
In [320]:
X
Out[320]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 1.297447 | 0.231356 | 0.546295 | 1.007345 | 0.269632 | 0.236051 |
| 1 | 0.766614 | -1.227377 | -0.605576 | -1.216250 | 0.473081 | -0.446139 |
| 2 | -0.431734 | -1.183099 | 1.287522 | 0.927452 | -0.309857 | -1.666006 |
| 3 | 0.078607 | -1.572049 | 1.188424 | 0.092330 | -0.244414 | -0.490911 |
| 4 | 0.007655 | -1.466562 | 1.294323 | -0.410616 | -0.763440 | -1.007284 |
| 5 | -0.370498 | 1.671450 | 0.153543 | -0.812481 | 0.183921 | -0.155363 |
| 6 | -0.291598 | 1.214994 | -0.454284 | -0.064902 | -0.043613 | 0.345369 |
| 7 | 0.069950 | -2.315347 | -1.489877 | -0.732147 | 0.555418 | -1.397283 |
| 8 | -0.492019 | 1.159708 | 0.098478 | 0.022986 | 1.057930 | 0.455568 |
| 9 | 0.452658 | -1.370382 | 0.122635 | 0.031601 | 0.705042 | -0.811405 |
| 10 | 0.178305 | -1.404896 | -0.110526 | -1.915899 | -0.893670 | -0.103585 |
| 11 | -0.394571 | -2.295623 | -0.401638 | 0.054717 | 0.112940 | -0.501624 |
| 12 | 0.825509 | -0.879700 | -0.340624 | -0.248308 | -0.147973 | -1.486938 |
| 13 | -0.567314 | -1.474896 | -0.684124 | -0.105587 | 0.149929 | 0.556946 |
| 14 | -0.287062 | -2.507554 | -0.996441 | -0.535391 | 0.704154 | -0.243508 |
| 15 | 1.589945 | 0.608356 | 1.711054 | -0.876066 | 0.069532 | -1.756202 |
| 16 | 1.546148 | -0.417533 | -1.674934 | 0.046415 | 0.405062 | 1.968764 |
| 17 | 1.404228 | -0.047254 | 0.691725 | 0.713455 | -0.813024 | -0.471725 |
| 18 | 0.518614 | -0.334892 | -1.333394 | 0.534502 | -0.127375 | 1.289634 |
| 19 | 1.235351 | 0.809799 | -0.698921 | 1.861197 | 0.360070 | 1.788748 |
| 20 | -0.483594 | -0.266831 | 0.102686 | -0.442555 | 0.306825 | 0.803359 |
| 21 | 0.728509 | -0.464576 | -1.661459 | -0.142280 | -0.515254 | 0.741121 |
| 22 | 1.170053 | 0.597229 | 1.612975 | 0.936085 | 0.020237 | -0.219170 |
| 23 | 1.225988 | -1.042734 | -2.231606 | -1.970862 | 0.162396 | 0.833329 |
| 24 | 0.272811 | 0.538009 | 0.918204 | 0.116495 | -1.927558 | 0.690336 |
| 25 | -0.521172 | 1.340198 | 1.481586 | -0.367734 | -0.838038 | 0.649490 |
| 26 | -0.188935 | 0.665974 | 1.392134 | -0.321225 | -2.463101 | 0.567261 |
| 27 | 1.519541 | -0.246954 | 1.208791 | 0.222868 | 0.071996 | -0.382862 |
| 28 | -0.405847 | -1.303832 | 1.440527 | -0.539141 | -1.285875 | -0.330782 |
| 29 | -1.850113 | -1.286361 | 0.526982 | -0.426757 | -0.821369 | -0.659039 |
| ... | ... | ... | ... | ... | ... | ... |
| 114 | 1.085296 | 1.141590 | -1.386728 | 0.289820 | -0.315495 | 1.792574 |
| 115 | -1.024369 | 0.443668 | -0.122627 | 0.442887 | 0.357498 | -1.433931 |
| 116 | 0.572112 | -1.055656 | 0.222883 | -0.373323 | -0.878489 | 1.874500 |
| 117 | 0.550237 | -0.766605 | -0.056746 | 0.016528 | -0.339377 | 0.722854 |
| 118 | 0.378441 | -1.897452 | -0.229383 | -2.155450 | -1.099087 | 1.240515 |
| 119 | -0.936242 | -0.306037 | -0.207792 | 1.249626 | -1.690910 | -1.306194 |
| 120 | 1.647950 | 0.514377 | -2.369044 | 0.073856 | -0.669624 | -0.740015 |
| 121 | 1.150939 | -0.633833 | -0.604698 | 1.176872 | 2.035262 | 0.080449 |
| 122 | 0.546853 | 1.514343 | 0.069751 | -1.901174 | 0.644701 | -1.269584 |
| 123 | 1.223435 | 0.897899 | 0.213625 | -0.937897 | 0.918787 | 0.720059 |
| 124 | 0.857062 | 0.842244 | -0.304539 | -0.782109 | -0.165739 | 0.507814 |
| 125 | 0.730334 | -0.675216 | 0.505721 | -0.782744 | -0.443136 | 0.211297 |
| 126 | 0.541766 | -0.279794 | 0.749254 | 0.499473 | -0.700209 | 0.809215 |
| 127 | 0.456191 | -0.974577 | 0.840615 | 0.567252 | -0.544141 | 0.020146 |
| 128 | 0.712574 | 0.049329 | 1.621927 | -0.707602 | -1.976844 | -0.222721 |
| 129 | 0.675521 | -0.012987 | 1.395059 | -1.145915 | -1.822364 | -0.863284 |
| 130 | -0.312450 | 1.433627 | 0.708713 | -2.164346 | -0.919893 | -0.306371 |
| 131 | 0.181615 | 1.164977 | -0.985505 | -0.574818 | -0.030196 | 0.270671 |
| 132 | 0.260510 | 0.988101 | -0.315543 | -1.769090 | -0.459089 | 0.211096 |
| 133 | -0.311371 | -1.408386 | -1.645724 | 0.532301 | 1.345514 | 0.165424 |
| 134 | -0.301269 | -0.950573 | -1.290638 | 0.868461 | 0.572738 | -0.467161 |
| 135 | -0.309830 | -0.407406 | 0.014910 | -0.223798 | 0.528946 | -0.355130 |
| 136 | -1.610060 | 0.941392 | -1.405020 | -0.222889 | -0.007353 | -0.446852 |
| 137 | -0.925268 | -0.067389 | -0.201548 | -0.622410 | 1.792638 | -1.539592 |
| 138 | -0.015264 | 0.452609 | -0.699062 | 1.150528 | 1.287409 | -0.173602 |
| 139 | 1.326980 | -1.383698 | 0.746727 | 1.072280 | -0.295723 | -1.047791 |
| 140 | 0.896216 | -1.267594 | 1.427336 | 0.997423 | 1.895405 | 0.117410 |
| 141 | 1.185098 | -0.497068 | 0.605539 | -1.180788 | -0.694937 | 0.254679 |
| 142 | 0.613731 | 1.209050 | -1.541221 | 0.828782 | -1.372158 | 0.505984 |
| 143 | -1.033863 | 0.780408 | 0.144446 | -0.151813 | -0.085778 | -0.271083 |
144 rows × 6 columns
In [321]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[321]:
[864.0000000000001, 718.4272269949738, 620.3882940000645, 547.0243831595473, 498.4088405289721, 452.2938075669829, 419.0812017663501, 394.96856155909757, 378.5884628409906, 357.339318590697, 344.59994179556793, 327.22045118404804, 316.0718756196303, 310.81264751973913]
In [322]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[322]:
[<matplotlib.lines.Line2D at 0x1b82c08cc18>]
K=4
In [323]:
kmeans_tc = KMeans(n_clusters=4, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[323]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [324]:
kmeans_tc.labels_
Out[324]:
array([3, 3, 3, 3, 3, 1, 1, 3, 1, 3, 3, 3, 3, 0, 0, 3, 0, 3, 0, 0, 0, 0,
3, 0, 3, 2, 3, 3, 3, 2, 3, 3, 0, 0, 2, 2, 0, 1, 0, 2, 1, 1, 1, 2,
2, 2, 0, 0, 0, 2, 2, 0, 1, 1, 1, 0, 3, 3, 0, 3, 0, 3, 2, 2, 2, 1,
1, 1, 3, 3, 1, 2, 0, 3, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 2, 3, 0,
3, 1, 1, 1, 1, 2, 0, 1, 0, 2, 1, 0, 2, 3, 1, 2, 2, 1, 2, 2, 2, 1,
2, 1, 0, 3, 0, 2, 0, 0, 3, 2, 0, 0, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1,
1, 0, 0, 1, 2, 1, 2, 3, 3, 3, 0, 2])
In [325]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[325]:
array([3, 3, 3, 3, 3, 1, 1, 3, 1, 3, 3, 3, 3, 0, 0, 3, 0, 3, 0, 0, 0, 0,
3, 0, 3, 2, 3, 3, 3, 2, 3, 3, 0, 0, 2, 2, 0, 1, 0, 2, 1, 1, 1, 2,
2, 2, 0, 0, 0, 2, 2, 0, 1, 1, 1, 0, 3, 3, 0, 3, 0, 3, 2, 2, 2, 1,
1, 1, 3, 3, 1, 2, 0, 3, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 2, 3, 0,
3, 1, 1, 1, 1, 2, 0, 1, 0, 2, 1, 0, 2, 3, 1, 2, 2, 1, 2, 2, 2, 1,
2, 1, 0, 3, 0, 2, 0, 0, 3, 2, 0, 0, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1,
1, 0, 0, 1, 2, 1, 2, 3, 3, 3, 0, 2])
In [326]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [327]:
X
Out[327]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1.297447 | 0.231356 | 0.546295 | 1.007345 | 0.269632 | 0.236051 | 3 | 0 |
| 1 | 0.766614 | -1.227377 | -0.605576 | -1.216250 | 0.473081 | -0.446139 | 3 | 0 |
| 2 | -0.431734 | -1.183099 | 1.287522 | 0.927452 | -0.309857 | -1.666006 | 3 | 0 |
| 3 | 0.078607 | -1.572049 | 1.188424 | 0.092330 | -0.244414 | -0.490911 | 3 | 0 |
| 4 | 0.007655 | -1.466562 | 1.294323 | -0.410616 | -0.763440 | -1.007284 | 3 | 0 |
| 5 | -0.370498 | 1.671450 | 0.153543 | -0.812481 | 0.183921 | -0.155363 | 1 | 0 |
| 6 | -0.291598 | 1.214994 | -0.454284 | -0.064902 | -0.043613 | 0.345369 | 1 | 0 |
| 7 | 0.069950 | -2.315347 | -1.489877 | -0.732147 | 0.555418 | -1.397283 | 3 | 0 |
| 8 | -0.492019 | 1.159708 | 0.098478 | 0.022986 | 1.057930 | 0.455568 | 1 | 0 |
| 9 | 0.452658 | -1.370382 | 0.122635 | 0.031601 | 0.705042 | -0.811405 | 3 | 0 |
| 10 | 0.178305 | -1.404896 | -0.110526 | -1.915899 | -0.893670 | -0.103585 | 3 | 0 |
| 11 | -0.394571 | -2.295623 | -0.401638 | 0.054717 | 0.112940 | -0.501624 | 3 | 0 |
| 12 | 0.825509 | -0.879700 | -0.340624 | -0.248308 | -0.147973 | -1.486938 | 3 | 0 |
| 13 | -0.567314 | -1.474896 | -0.684124 | -0.105587 | 0.149929 | 0.556946 | 0 | 0 |
| 14 | -0.287062 | -2.507554 | -0.996441 | -0.535391 | 0.704154 | -0.243508 | 0 | 0 |
| 15 | 1.589945 | 0.608356 | 1.711054 | -0.876066 | 0.069532 | -1.756202 | 3 | 0 |
| 16 | 1.546148 | -0.417533 | -1.674934 | 0.046415 | 0.405062 | 1.968764 | 0 | 0 |
| 17 | 1.404228 | -0.047254 | 0.691725 | 0.713455 | -0.813024 | -0.471725 | 3 | 0 |
| 18 | 0.518614 | -0.334892 | -1.333394 | 0.534502 | -0.127375 | 1.289634 | 0 | 0 |
| 19 | 1.235351 | 0.809799 | -0.698921 | 1.861197 | 0.360070 | 1.788748 | 0 | 0 |
| 20 | -0.483594 | -0.266831 | 0.102686 | -0.442555 | 0.306825 | 0.803359 | 0 | 0 |
| 21 | 0.728509 | -0.464576 | -1.661459 | -0.142280 | -0.515254 | 0.741121 | 0 | 0 |
| 22 | 1.170053 | 0.597229 | 1.612975 | 0.936085 | 0.020237 | -0.219170 | 3 | 0 |
| 23 | 1.225988 | -1.042734 | -2.231606 | -1.970862 | 0.162396 | 0.833329 | 0 | 0 |
| 24 | 0.272811 | 0.538009 | 0.918204 | 0.116495 | -1.927558 | 0.690336 | 3 | 0 |
| 25 | -0.521172 | 1.340198 | 1.481586 | -0.367734 | -0.838038 | 0.649490 | 2 | 0 |
| 26 | -0.188935 | 0.665974 | 1.392134 | -0.321225 | -2.463101 | 0.567261 | 3 | 0 |
| 27 | 1.519541 | -0.246954 | 1.208791 | 0.222868 | 0.071996 | -0.382862 | 3 | 0 |
| 28 | -0.405847 | -1.303832 | 1.440527 | -0.539141 | -1.285875 | -0.330782 | 3 | 0 |
| 29 | -1.850113 | -1.286361 | 0.526982 | -0.426757 | -0.821369 | -0.659039 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 114 | 1.085296 | 1.141590 | -1.386728 | 0.289820 | -0.315495 | 1.792574 | 0 | 1 |
| 115 | -1.024369 | 0.443668 | -0.122627 | 0.442887 | 0.357498 | -1.433931 | 2 | 1 |
| 116 | 0.572112 | -1.055656 | 0.222883 | -0.373323 | -0.878489 | 1.874500 | 0 | 1 |
| 117 | 0.550237 | -0.766605 | -0.056746 | 0.016528 | -0.339377 | 0.722854 | 0 | 1 |
| 118 | 0.378441 | -1.897452 | -0.229383 | -2.155450 | -1.099087 | 1.240515 | 3 | 1 |
| 119 | -0.936242 | -0.306037 | -0.207792 | 1.249626 | -1.690910 | -1.306194 | 2 | 1 |
| 120 | 1.647950 | 0.514377 | -2.369044 | 0.073856 | -0.669624 | -0.740015 | 0 | 1 |
| 121 | 1.150939 | -0.633833 | -0.604698 | 1.176872 | 2.035262 | 0.080449 | 0 | 1 |
| 122 | 0.546853 | 1.514343 | 0.069751 | -1.901174 | 0.644701 | -1.269584 | 1 | 1 |
| 123 | 1.223435 | 0.897899 | 0.213625 | -0.937897 | 0.918787 | 0.720059 | 1 | 1 |
| 124 | 0.857062 | 0.842244 | -0.304539 | -0.782109 | -0.165739 | 0.507814 | 1 | 1 |
| 125 | 0.730334 | -0.675216 | 0.505721 | -0.782744 | -0.443136 | 0.211297 | 3 | 1 |
| 126 | 0.541766 | -0.279794 | 0.749254 | 0.499473 | -0.700209 | 0.809215 | 3 | 1 |
| 127 | 0.456191 | -0.974577 | 0.840615 | 0.567252 | -0.544141 | 0.020146 | 3 | 1 |
| 128 | 0.712574 | 0.049329 | 1.621927 | -0.707602 | -1.976844 | -0.222721 | 3 | 1 |
| 129 | 0.675521 | -0.012987 | 1.395059 | -1.145915 | -1.822364 | -0.863284 | 3 | 1 |
| 130 | -0.312450 | 1.433627 | 0.708713 | -2.164346 | -0.919893 | -0.306371 | 1 | 1 |
| 131 | 0.181615 | 1.164977 | -0.985505 | -0.574818 | -0.030196 | 0.270671 | 1 | 1 |
| 132 | 0.260510 | 0.988101 | -0.315543 | -1.769090 | -0.459089 | 0.211096 | 1 | 1 |
| 133 | -0.311371 | -1.408386 | -1.645724 | 0.532301 | 1.345514 | 0.165424 | 0 | 1 |
| 134 | -0.301269 | -0.950573 | -1.290638 | 0.868461 | 0.572738 | -0.467161 | 0 | 1 |
| 135 | -0.309830 | -0.407406 | 0.014910 | -0.223798 | 0.528946 | -0.355130 | 1 | 1 |
| 136 | -1.610060 | 0.941392 | -1.405020 | -0.222889 | -0.007353 | -0.446852 | 2 | 1 |
| 137 | -0.925268 | -0.067389 | -0.201548 | -0.622410 | 1.792638 | -1.539592 | 1 | 1 |
| 138 | -0.015264 | 0.452609 | -0.699062 | 1.150528 | 1.287409 | -0.173602 | 2 | 1 |
| 139 | 1.326980 | -1.383698 | 0.746727 | 1.072280 | -0.295723 | -1.047791 | 3 | 1 |
| 140 | 0.896216 | -1.267594 | 1.427336 | 0.997423 | 1.895405 | 0.117410 | 3 | 1 |
| 141 | 1.185098 | -0.497068 | 0.605539 | -1.180788 | -0.694937 | 0.254679 | 3 | 1 |
| 142 | 0.613731 | 1.209050 | -1.541221 | 0.828782 | -1.372158 | 0.505984 | 0 | 1 |
| 143 | -1.033863 | 0.780408 | 0.144446 | -0.151813 | -0.085778 | -0.271083 | 2 | 1 |
144 rows × 8 columns
In [328]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[328]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b82c211b00>
In [329]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[4]))
Specialized¶
ANN¶
In [217]:
X = df_n_ps_std_tc[4]
In [218]:
y = df_n_ps[4]['chosen']
In [219]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [220]:
X_train.shape
Out[220]:
(164, 6)
In [221]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [222]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [223]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [337]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (20, 20), 'learning_rate_init': 0.01, 'max_iter': 300}, que permiten obtener un Accuracy de 68.29% y un Kappa del 36.07
Tiempo total: 25.13 minutos
In [224]:
grid.best_params_={'activation': 'tanh', 'hidden_layer_sizes': (20, 20), 'learning_rate_init': 0.01, 'max_iter': 300}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [225]:
input_tensor = Input(shape = (n0,))
In [226]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [227]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [228]:
model.summary()
Model: "model_11" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_12 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_31 (Dense) (None, 20) 140 _________________________________________________________________ dense_32 (Dense) (None, 20) 420 _________________________________________________________________ dense_33 (Dense) (None, 1) 21 ================================================================= Total params: 581 Trainable params: 581 Non-trainable params: 0 _________________________________________________________________
In [229]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 164 samples, validate on 55 samples Epoch 1/300 164/164 [==============================] - 0s 1ms/step - loss: 0.7152 - accuracy: 0.4939 - val_loss: 0.6864 - val_accuracy: 0.6182 Epoch 2/300 164/164 [==============================] - 0s 98us/step - loss: 0.6924 - accuracy: 0.5793 - val_loss: 0.6894 - val_accuracy: 0.5455 Epoch 3/300 164/164 [==============================] - 0s 104us/step - loss: 0.6870 - accuracy: 0.5793 - val_loss: 0.6866 - val_accuracy: 0.6182 Epoch 4/300 164/164 [==============================] - 0s 116us/step - loss: 0.6788 - accuracy: 0.5488 - val_loss: 0.6931 - val_accuracy: 0.5818 Epoch 5/300 164/164 [==============================] - 0s 98us/step - loss: 0.6804 - accuracy: 0.5061 - val_loss: 0.6920 - val_accuracy: 0.5818 Epoch 6/300 164/164 [==============================] - 0s 91us/step - loss: 0.6784 - accuracy: 0.5244 - val_loss: 0.6955 - val_accuracy: 0.5818 Epoch 7/300 164/164 [==============================] - 0s 97us/step - loss: 0.6729 - accuracy: 0.5793 - val_loss: 0.6993 - val_accuracy: 0.5091 Epoch 8/300 164/164 [==============================] - 0s 97us/step - loss: 0.6683 - accuracy: 0.6159 - val_loss: 0.6967 - val_accuracy: 0.5818 Epoch 9/300 164/164 [==============================] - 0s 104us/step - loss: 0.6658 - accuracy: 0.5915 - val_loss: 0.6956 - val_accuracy: 0.6182 Epoch 10/300 164/164 [==============================] - 0s 116us/step - loss: 0.6646 - accuracy: 0.5793 - val_loss: 0.6990 - val_accuracy: 0.5818 Epoch 11/300 164/164 [==============================] - 0s 104us/step - loss: 0.6635 - accuracy: 0.6037 - val_loss: 0.6999 - val_accuracy: 0.5636 Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.004999999888241291. Epoch 12/300 164/164 [==============================] - 0s 116us/step - loss: 0.6595 - accuracy: 0.5793 - val_loss: 0.6960 - val_accuracy: 0.5818 Epoch 13/300 164/164 [==============================] - 0s 116us/step - loss: 0.6566 - accuracy: 0.6037 - val_loss: 0.6901 - val_accuracy: 0.6000 Epoch 14/300 164/164 [==============================] - 0s 110us/step - loss: 0.6541 - accuracy: 0.6159 - val_loss: 0.6938 - val_accuracy: 0.5636 Epoch 15/300 164/164 [==============================] - 0s 104us/step - loss: 0.6497 - accuracy: 0.6037 - val_loss: 0.6901 - val_accuracy: 0.5818 Epoch 16/300 164/164 [==============================] - 0s 104us/step - loss: 0.6474 - accuracy: 0.5976 - val_loss: 0.6848 - val_accuracy: 0.6000 Epoch 17/300 164/164 [==============================] - 0s 98us/step - loss: 0.6439 - accuracy: 0.6220 - val_loss: 0.6840 - val_accuracy: 0.6727 Epoch 18/300 164/164 [==============================] - 0s 110us/step - loss: 0.6450 - accuracy: 0.6341 - val_loss: 0.6861 - val_accuracy: 0.6545 Epoch 19/300 164/164 [==============================] - 0s 97us/step - loss: 0.6410 - accuracy: 0.6098 - val_loss: 0.6874 - val_accuracy: 0.5818 Epoch 20/300 164/164 [==============================] - 0s 104us/step - loss: 0.6380 - accuracy: 0.6524 - val_loss: 0.6909 - val_accuracy: 0.5455 Epoch 21/300 164/164 [==============================] - 0s 116us/step - loss: 0.6363 - accuracy: 0.6402 - val_loss: 0.6940 - val_accuracy: 0.5455 Epoch 22/300 164/164 [==============================] - 0s 134us/step - loss: 0.6313 - accuracy: 0.6524 - val_loss: 0.6876 - val_accuracy: 0.6364 Epoch 23/300 164/164 [==============================] - 0s 128us/step - loss: 0.6251 - accuracy: 0.6707 - val_loss: 0.6850 - val_accuracy: 0.6182 Epoch 24/300 164/164 [==============================] - 0s 134us/step - loss: 0.6238 - accuracy: 0.6707 - val_loss: 0.6887 - val_accuracy: 0.6182 Epoch 25/300 164/164 [==============================] - 0s 128us/step - loss: 0.6220 - accuracy: 0.6646 - val_loss: 0.6957 - val_accuracy: 0.6364 Epoch 26/300 164/164 [==============================] - 0s 134us/step - loss: 0.6164 - accuracy: 0.6768 - val_loss: 0.7036 - val_accuracy: 0.6000 Epoch 27/300 164/164 [==============================] - 0s 116us/step - loss: 0.6151 - accuracy: 0.7134 - val_loss: 0.7244 - val_accuracy: 0.6182 Epoch 00027: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455. Epoch 28/300 164/164 [==============================] - 0s 122us/step - loss: 0.6133 - accuracy: 0.6890 - val_loss: 0.7313 - val_accuracy: 0.6182 Epoch 29/300 164/164 [==============================] - 0s 116us/step - loss: 0.6162 - accuracy: 0.6829 - val_loss: 0.7357 - val_accuracy: 0.6182 Epoch 30/300 164/164 [==============================] - 0s 128us/step - loss: 0.6105 - accuracy: 0.7134 - val_loss: 0.7280 - val_accuracy: 0.6000 Epoch 31/300 164/164 [==============================] - 0s 122us/step - loss: 0.6073 - accuracy: 0.7073 - val_loss: 0.7206 - val_accuracy: 0.5455 Epoch 32/300 164/164 [==============================] - 0s 104us/step - loss: 0.6039 - accuracy: 0.7378 - val_loss: 0.7226 - val_accuracy: 0.5091 Epoch 33/300 164/164 [==============================] - 0s 104us/step - loss: 0.5988 - accuracy: 0.7317 - val_loss: 0.7280 - val_accuracy: 0.5091 Epoch 34/300 164/164 [==============================] - 0s 110us/step - loss: 0.5988 - accuracy: 0.7439 - val_loss: 0.7300 - val_accuracy: 0.5273 Epoch 35/300 164/164 [==============================] - 0s 104us/step - loss: 0.5953 - accuracy: 0.7378 - val_loss: 0.7286 - val_accuracy: 0.5818 Epoch 36/300 164/164 [==============================] - 0s 110us/step - loss: 0.5917 - accuracy: 0.7256 - val_loss: 0.7280 - val_accuracy: 0.6182 Epoch 37/300 164/164 [==============================] - 0s 110us/step - loss: 0.5916 - accuracy: 0.7256 - val_loss: 0.7269 - val_accuracy: 0.6182 Epoch 00037: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228. Epoch 38/300 164/164 [==============================] - 0s 98us/step - loss: 0.5880 - accuracy: 0.7195 - val_loss: 0.7256 - val_accuracy: 0.5455 Epoch 39/300 164/164 [==============================] - 0s 104us/step - loss: 0.5864 - accuracy: 0.7256 - val_loss: 0.7251 - val_accuracy: 0.5455 Epoch 40/300 164/164 [==============================] - 0s 104us/step - loss: 0.5853 - accuracy: 0.7256 - val_loss: 0.7253 - val_accuracy: 0.5273 Epoch 41/300 164/164 [==============================] - 0s 104us/step - loss: 0.5854 - accuracy: 0.7195 - val_loss: 0.7244 - val_accuracy: 0.5273 Epoch 42/300 164/164 [==============================] - 0s 97us/step - loss: 0.5828 - accuracy: 0.7134 - val_loss: 0.7246 - val_accuracy: 0.5636 Epoch 43/300 164/164 [==============================] - 0s 104us/step - loss: 0.5826 - accuracy: 0.7195 - val_loss: 0.7240 - val_accuracy: 0.5636 Epoch 44/300 164/164 [==============================] - 0s 98us/step - loss: 0.5819 - accuracy: 0.7134 - val_loss: 0.7220 - val_accuracy: 0.6364 Epoch 45/300 164/164 [==============================] - ETA: 0s - loss: 0.6176 - accuracy: 0.65 - 0s 128us/step - loss: 0.5803 - accuracy: 0.7256 - val_loss: 0.7210 - val_accuracy: 0.6364 Epoch 46/300 164/164 [==============================] - 0s 104us/step - loss: 0.5800 - accuracy: 0.7195 - val_loss: 0.7208 - val_accuracy: 0.5818 Epoch 47/300 164/164 [==============================] - 0s 104us/step - loss: 0.5783 - accuracy: 0.7195 - val_loss: 0.7210 - val_accuracy: 0.5818 Epoch 00047: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614. Epoch 48/300 164/164 [==============================] - 0s 110us/step - loss: 0.5772 - accuracy: 0.7378 - val_loss: 0.7217 - val_accuracy: 0.5818 Epoch 49/300 164/164 [==============================] - 0s 104us/step - loss: 0.5769 - accuracy: 0.7378 - val_loss: 0.7219 - val_accuracy: 0.6000 Epoch 50/300 164/164 [==============================] - 0s 91us/step - loss: 0.5770 - accuracy: 0.7378 - val_loss: 0.7218 - val_accuracy: 0.5818 Epoch 51/300 164/164 [==============================] - 0s 104us/step - loss: 0.5763 - accuracy: 0.7378 - val_loss: 0.7214 - val_accuracy: 0.6000 Epoch 52/300 164/164 [==============================] - 0s 104us/step - loss: 0.5762 - accuracy: 0.7256 - val_loss: 0.7220 - val_accuracy: 0.6000 Epoch 53/300 164/164 [==============================] - 0s 116us/step - loss: 0.5756 - accuracy: 0.7378 - val_loss: 0.7236 - val_accuracy: 0.6000 Epoch 54/300 164/164 [==============================] - 0s 116us/step - loss: 0.5746 - accuracy: 0.7439 - val_loss: 0.7252 - val_accuracy: 0.6000 Epoch 55/300 164/164 [==============================] - 0s 116us/step - loss: 0.5737 - accuracy: 0.7439 - val_loss: 0.7268 - val_accuracy: 0.5818 Epoch 56/300 164/164 [==============================] - 0s 110us/step - loss: 0.5733 - accuracy: 0.7439 - val_loss: 0.7271 - val_accuracy: 0.6000 Epoch 57/300 164/164 [==============================] - 0s 104us/step - loss: 0.5718 - accuracy: 0.7439 - val_loss: 0.7275 - val_accuracy: 0.6000 Epoch 00057: ReduceLROnPlateau reducing learning rate to 0.0003124999930150807. Epoch 58/300 164/164 [==============================] - 0s 104us/step - loss: 0.5712 - accuracy: 0.7439 - val_loss: 0.7281 - val_accuracy: 0.6000 Epoch 59/300 164/164 [==============================] - 0s 110us/step - loss: 0.5708 - accuracy: 0.7378 - val_loss: 0.7285 - val_accuracy: 0.6000 Epoch 60/300 164/164 [==============================] - 0s 110us/step - loss: 0.5707 - accuracy: 0.7500 - val_loss: 0.7293 - val_accuracy: 0.5818 Epoch 61/300 164/164 [==============================] - 0s 104us/step - loss: 0.5704 - accuracy: 0.7561 - val_loss: 0.7298 - val_accuracy: 0.5818 Epoch 62/300 164/164 [==============================] - 0s 104us/step - loss: 0.5700 - accuracy: 0.7561 - val_loss: 0.7297 - val_accuracy: 0.6000 Epoch 63/300 164/164 [==============================] - 0s 110us/step - loss: 0.5696 - accuracy: 0.7500 - val_loss: 0.7296 - val_accuracy: 0.6000 Epoch 64/300 164/164 [==============================] - 0s 98us/step - loss: 0.5693 - accuracy: 0.7378 - val_loss: 0.7300 - val_accuracy: 0.5636 Epoch 65/300 164/164 [==============================] - 0s 104us/step - loss: 0.5691 - accuracy: 0.7439 - val_loss: 0.7303 - val_accuracy: 0.5455 Epoch 66/300 164/164 [==============================] - 0s 98us/step - loss: 0.5685 - accuracy: 0.7439 - val_loss: 0.7303 - val_accuracy: 0.5636 Epoch 67/300 164/164 [==============================] - 0s 98us/step - loss: 0.5687 - accuracy: 0.7317 - val_loss: 0.7304 - val_accuracy: 0.5636 Epoch 00067: ReduceLROnPlateau reducing learning rate to 0.00015624999650754035. Epoch 68/300 164/164 [==============================] - 0s 104us/step - loss: 0.5681 - accuracy: 0.7317 - val_loss: 0.7303 - val_accuracy: 0.5636 Epoch 69/300 164/164 [==============================] - 0s 104us/step - loss: 0.5681 - accuracy: 0.7317 - val_loss: 0.7301 - val_accuracy: 0.5636 Epoch 70/300 164/164 [==============================] - 0s 98us/step - loss: 0.5680 - accuracy: 0.7317 - val_loss: 0.7302 - val_accuracy: 0.5636 Epoch 71/300 164/164 [==============================] - 0s 116us/step - loss: 0.5677 - accuracy: 0.7317 - val_loss: 0.7303 - val_accuracy: 0.5818 Epoch 72/300 164/164 [==============================] - 0s 104us/step - loss: 0.5676 - accuracy: 0.7317 - val_loss: 0.7305 - val_accuracy: 0.5636 Epoch 73/300 164/164 [==============================] - 0s 98us/step - loss: 0.5674 - accuracy: 0.7317 - val_loss: 0.7309 - val_accuracy: 0.5636 Epoch 74/300 164/164 [==============================] - 0s 98us/step - loss: 0.5671 - accuracy: 0.7317 - val_loss: 0.7312 - val_accuracy: 0.5636 Epoch 75/300 164/164 [==============================] - 0s 110us/step - loss: 0.5669 - accuracy: 0.7317 - val_loss: 0.7316 - val_accuracy: 0.5636 Epoch 76/300 164/164 [==============================] - 0s 104us/step - loss: 0.5668 - accuracy: 0.7317 - val_loss: 0.7318 - val_accuracy: 0.5636 Epoch 77/300 164/164 [==============================] - 0s 104us/step - loss: 0.5666 - accuracy: 0.7317 - val_loss: 0.7318 - val_accuracy: 0.5818 Epoch 00077: ReduceLROnPlateau reducing learning rate to 7.812499825377017e-05. Epoch 78/300 164/164 [==============================] - 0s 110us/step - loss: 0.5665 - accuracy: 0.7317 - val_loss: 0.7320 - val_accuracy: 0.5818 Epoch 79/300 164/164 [==============================] - 0s 91us/step - loss: 0.5664 - accuracy: 0.7317 - val_loss: 0.7320 - val_accuracy: 0.5818 Epoch 80/300 164/164 [==============================] - 0s 104us/step - loss: 0.5663 - accuracy: 0.7317 - val_loss: 0.7320 - val_accuracy: 0.5818 Epoch 81/300 164/164 [==============================] - 0s 110us/step - loss: 0.5662 - accuracy: 0.7317 - val_loss: 0.7319 - val_accuracy: 0.5818 Epoch 82/300 164/164 [==============================] - 0s 98us/step - loss: 0.5661 - accuracy: 0.7317 - val_loss: 0.7321 - val_accuracy: 0.5818 Epoch 83/300 164/164 [==============================] - 0s 122us/step - loss: 0.5661 - accuracy: 0.7317 - val_loss: 0.7323 - val_accuracy: 0.5818 Epoch 84/300 164/164 [==============================] - 0s 110us/step - loss: 0.5660 - accuracy: 0.7256 - val_loss: 0.7324 - val_accuracy: 0.5818 Epoch 85/300 164/164 [==============================] - 0s 104us/step - loss: 0.5659 - accuracy: 0.7317 - val_loss: 0.7325 - val_accuracy: 0.5818 Epoch 86/300 164/164 [==============================] - 0s 110us/step - loss: 0.5659 - accuracy: 0.7378 - val_loss: 0.7325 - val_accuracy: 0.5818 Epoch 87/300 164/164 [==============================] - 0s 104us/step - loss: 0.5657 - accuracy: 0.7378 - val_loss: 0.7325 - val_accuracy: 0.5818 Epoch 00087: ReduceLROnPlateau reducing learning rate to 3.9062499126885086e-05. Epoch 88/300 164/164 [==============================] - 0s 134us/step - loss: 0.5657 - accuracy: 0.7378 - val_loss: 0.7325 - val_accuracy: 0.5636 Epoch 89/300 164/164 [==============================] - 0s 140us/step - loss: 0.5656 - accuracy: 0.7378 - val_loss: 0.7325 - val_accuracy: 0.5636 Epoch 90/300 164/164 [==============================] - 0s 140us/step - loss: 0.5656 - accuracy: 0.7378 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 91/300 164/164 [==============================] - 0s 134us/step - loss: 0.5656 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 92/300 164/164 [==============================] - 0s 122us/step - loss: 0.5655 - accuracy: 0.7439 - val_loss: 0.7328 - val_accuracy: 0.5636 Epoch 93/300 164/164 [==============================] - 0s 122us/step - loss: 0.5655 - accuracy: 0.7439 - val_loss: 0.7328 - val_accuracy: 0.5636 Epoch 94/300 164/164 [==============================] - 0s 122us/step - loss: 0.5654 - accuracy: 0.7439 - val_loss: 0.7328 - val_accuracy: 0.5636 Epoch 95/300 164/164 [==============================] - 0s 122us/step - loss: 0.5653 - accuracy: 0.7439 - val_loss: 0.7328 - val_accuracy: 0.5636 Epoch 96/300 164/164 [==============================] - 0s 128us/step - loss: 0.5653 - accuracy: 0.7439 - val_loss: 0.7328 - val_accuracy: 0.5636 Epoch 97/300 164/164 [==============================] - 0s 122us/step - loss: 0.5652 - accuracy: 0.7439 - val_loss: 0.7328 - val_accuracy: 0.5636 Epoch 00097: ReduceLROnPlateau reducing learning rate to 1.9531249563442543e-05. Epoch 98/300 164/164 [==============================] - 0s 128us/step - loss: 0.5652 - accuracy: 0.7439 - val_loss: 0.7328 - val_accuracy: 0.5636 Epoch 99/300 164/164 [==============================] - 0s 110us/step - loss: 0.5652 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 100/300 164/164 [==============================] - 0s 116us/step - loss: 0.5652 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 101/300 164/164 [==============================] - 0s 116us/step - loss: 0.5652 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 102/300 164/164 [==============================] - 0s 110us/step - loss: 0.5652 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 103/300 164/164 [==============================] - 0s 110us/step - loss: 0.5651 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 104/300 164/164 [==============================] - 0s 116us/step - loss: 0.5651 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 105/300 164/164 [==============================] - 0s 134us/step - loss: 0.5651 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 106/300 164/164 [==============================] - 0s 128us/step - loss: 0.5651 - accuracy: 0.7439 - val_loss: 0.7325 - val_accuracy: 0.5636 Epoch 107/300 164/164 [==============================] - 0s 122us/step - loss: 0.5651 - accuracy: 0.7439 - val_loss: 0.7325 - val_accuracy: 0.5636 Epoch 00107: ReduceLROnPlateau reducing learning rate to 9.765624781721272e-06. Epoch 108/300 164/164 [==============================] - 0s 134us/step - loss: 0.5650 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 109/300 164/164 [==============================] - 0s 134us/step - loss: 0.5650 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 110/300 164/164 [==============================] - 0s 122us/step - loss: 0.5650 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 111/300 164/164 [==============================] - 0s 140us/step - loss: 0.5650 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 112/300 164/164 [==============================] - 0s 128us/step - loss: 0.5650 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 113/300 164/164 [==============================] - 0s 140us/step - loss: 0.5650 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 114/300 164/164 [==============================] - 0s 128us/step - loss: 0.5650 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 115/300 164/164 [==============================] - 0s 165us/step - loss: 0.5650 - accuracy: 0.7439 - val_loss: 0.7326 - val_accuracy: 0.5636 Epoch 116/300 164/164 [==============================] - 0s 165us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 117/300 164/164 [==============================] - 0s 122us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00117: ReduceLROnPlateau reducing learning rate to 4.882812390860636e-06. Epoch 118/300 164/164 [==============================] - 0s 134us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 119/300 164/164 [==============================] - 0s 122us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 120/300 164/164 [==============================] - 0s 134us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 121/300 164/164 [==============================] - 0s 140us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 122/300 164/164 [==============================] - 0s 128us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 123/300 164/164 [==============================] - 0s 134us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 124/300 164/164 [==============================] - 0s 128us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 125/300 164/164 [==============================] - 0s 158us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 126/300 164/164 [==============================] - 0s 128us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 127/300 164/164 [==============================] - 0s 116us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00127: ReduceLROnPlateau reducing learning rate to 2.441406195430318e-06. Epoch 128/300 164/164 [==============================] - 0s 122us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 129/300 164/164 [==============================] - 0s 116us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 130/300 164/164 [==============================] - 0s 116us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 131/300 164/164 [==============================] - 0s 146us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 132/300 164/164 [==============================] - 0s 128us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 133/300 164/164 [==============================] - 0s 140us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 134/300 164/164 [==============================] - 0s 116us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 135/300 164/164 [==============================] - 0s 110us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 136/300 164/164 [==============================] - 0s 122us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 137/300 164/164 [==============================] - 0s 116us/step - loss: 0.5649 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00137: ReduceLROnPlateau reducing learning rate to 1.220703097715159e-06. Epoch 138/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 139/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 140/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 141/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 142/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 143/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 144/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 145/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 146/300 164/164 [==============================] - 0s 152us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 147/300 164/164 [==============================] - 0s 146us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00147: ReduceLROnPlateau reducing learning rate to 6.103515488575795e-07. Epoch 148/300 164/164 [==============================] - 0s 140us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 149/300 164/164 [==============================] - 0s 140us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 150/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 151/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 152/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 153/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 154/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 155/300 164/164 [==============================] - 0s 140us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 156/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 157/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00157: ReduceLROnPlateau reducing learning rate to 3.0517577442878974e-07. Epoch 158/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 159/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 160/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 161/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 162/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 163/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 164/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 165/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 166/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 167/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00167: ReduceLROnPlateau reducing learning rate to 1.5258788721439487e-07. Epoch 168/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 169/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 170/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 171/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 172/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 173/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 174/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 175/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 176/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 177/300 164/164 [==============================] - 0s 152us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00177: ReduceLROnPlateau reducing learning rate to 7.629394360719743e-08. Epoch 178/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 179/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 180/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 181/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 182/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 183/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 184/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 185/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 186/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 187/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00187: ReduceLROnPlateau reducing learning rate to 3.814697180359872e-08. Epoch 188/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 189/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 190/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 191/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 192/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 193/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 194/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 195/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 196/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 197/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00197: ReduceLROnPlateau reducing learning rate to 1.907348590179936e-08. Epoch 198/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 199/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 200/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 201/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 202/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 203/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 204/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 205/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 206/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 207/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00207: ReduceLROnPlateau reducing learning rate to 9.53674295089968e-09. Epoch 208/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 209/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 210/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 211/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 212/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 213/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 214/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 215/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 216/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 217/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00217: ReduceLROnPlateau reducing learning rate to 4.76837147544984e-09. Epoch 218/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 219/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 220/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 221/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 222/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 223/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 224/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 225/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 226/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 227/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00227: ReduceLROnPlateau reducing learning rate to 2.38418573772492e-09. Epoch 228/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 229/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 230/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 231/300 164/164 [==============================] - 0s 140us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 232/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 233/300 164/164 [==============================] - 0s 122us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 234/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 235/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 236/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 237/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00237: ReduceLROnPlateau reducing learning rate to 1.19209286886246e-09. Epoch 238/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 239/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 240/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 241/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 242/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 243/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 244/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 245/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 246/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 247/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00247: ReduceLROnPlateau reducing learning rate to 5.9604643443123e-10. Epoch 248/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 249/300 164/164 [==============================] - 0s 91us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 250/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 251/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 252/300 164/164 [==============================] - 0s 91us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 253/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 254/300 164/164 [==============================] - 0s 91us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 255/300 164/164 [==============================] - 0s 97us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 256/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 257/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00257: ReduceLROnPlateau reducing learning rate to 2.98023217215615e-10. Epoch 258/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 259/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 260/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 261/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 262/300 164/164 [==============================] - 0s 97us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 263/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 264/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 265/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 266/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 267/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00267: ReduceLROnPlateau reducing learning rate to 1.490116086078075e-10. Epoch 268/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 269/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 270/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 271/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 272/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 273/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 274/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 275/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 276/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 277/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00277: ReduceLROnPlateau reducing learning rate to 7.450580430390374e-11. Epoch 278/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 279/300 164/164 [==============================] - 0s 128us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 280/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 281/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 282/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 283/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 284/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 285/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 286/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 287/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00287: ReduceLROnPlateau reducing learning rate to 3.725290215195187e-11. Epoch 288/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 289/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 290/300 164/164 [==============================] - 0s 140us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 291/300 164/164 [==============================] - 0s 116us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 292/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 293/300 164/164 [==============================] - 0s 110us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 294/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 295/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 296/300 164/164 [==============================] - 0s 134us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 297/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 00297: ReduceLROnPlateau reducing learning rate to 1.8626451075975936e-11. Epoch 298/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 299/300 164/164 [==============================] - 0s 104us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636 Epoch 300/300 164/164 [==============================] - 0s 98us/step - loss: 0.5648 - accuracy: 0.7439 - val_loss: 0.7327 - val_accuracy: 0.5636
In [230]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 300)
In [231]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
55/55 [==============================] - 0s 55us/step test loss: 0.732729638706554, test accuracy: 0.5636363625526428
In [232]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.5621693121693122
In [233]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.12350597609561753 [[19 9] [15 12]]
KMeans¶
In [348]:
X
Out[348]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | -1.035481 | 1.779354 | 1.874576 | 0.924814 | -0.129662 | 2.608421 |
| 1 | 0.965487 | -0.399971 | -1.606069 | 0.008311 | 0.834341 | 0.513694 |
| 2 | -0.141249 | -1.969933 | -0.960470 | 1.005123 | -1.117123 | -2.399517 |
| 3 | -1.590590 | -0.729741 | -0.575342 | 0.587988 | -0.885561 | -0.752828 |
| 4 | -0.391524 | -0.894181 | -0.426309 | 1.017585 | -0.391173 | -0.920259 |
| 5 | -1.256622 | -0.886861 | -0.850243 | 0.516749 | -0.491454 | -0.072867 |
| 6 | -1.579202 | 0.121365 | -0.522749 | 1.012025 | -0.547676 | -0.140430 |
| 7 | -1.760350 | -0.182429 | -0.008789 | 1.576085 | -0.878841 | 0.252104 |
| 8 | 1.115526 | 1.555384 | 0.609404 | 0.558809 | -0.514428 | -0.221726 |
| 9 | 1.467291 | 1.402697 | 0.806896 | -0.279535 | 0.939735 | 0.758333 |
| 10 | 0.972379 | 1.550575 | -0.223468 | 0.899199 | 1.412818 | 0.386724 |
| 11 | 0.294385 | 0.890870 | 0.493531 | 0.142145 | 0.212432 | 1.463886 |
| 12 | 0.795134 | 0.176458 | 1.588747 | -0.412034 | -0.982878 | -0.299581 |
| 13 | 0.694481 | 0.577820 | 0.319393 | 0.451356 | 0.219257 | 0.563199 |
| 14 | 1.169114 | 0.075245 | -0.980006 | 1.330732 | 2.094068 | 1.785970 |
| 15 | 0.962642 | 0.380225 | -1.261850 | 1.044019 | 1.339949 | 1.776870 |
| 16 | 1.352245 | 0.463507 | -0.679184 | 0.941519 | 2.196602 | 1.991369 |
| 17 | 1.784002 | -1.453636 | -1.128885 | -0.626496 | 0.399672 | -0.605861 |
| 18 | 0.929212 | -0.538274 | -1.016394 | -0.167176 | 0.557933 | 1.511009 |
| 19 | 1.199761 | -0.727252 | 0.322239 | -1.105069 | -0.125311 | -0.979170 |
| 20 | -0.485056 | 0.796900 | 0.581966 | 1.884586 | -0.705890 | -0.725300 |
| 21 | -0.547233 | 0.692440 | -0.162284 | 2.025268 | -0.631876 | -0.337240 |
| 22 | 1.446103 | -0.074850 | -0.132752 | -0.064117 | -0.209506 | -0.465551 |
| 23 | -0.312063 | 0.030270 | -1.160963 | 0.726155 | -1.511552 | -0.509175 |
| 24 | 1.175126 | -0.143713 | -0.522479 | 0.641015 | 0.500311 | 0.617748 |
| 25 | -1.044292 | -0.058933 | -1.340279 | -1.302246 | 1.751828 | -0.815403 |
| 26 | -0.849044 | 0.079838 | -0.400536 | -1.312330 | 1.498217 | -0.550869 |
| 27 | -0.730672 | -0.326196 | -0.478608 | -0.832610 | -0.556236 | -0.653280 |
| 28 | -0.380922 | -0.892886 | -0.555313 | -0.113628 | 1.211258 | -0.901155 |
| 29 | -0.368302 | -1.168844 | -0.094765 | -0.158075 | 1.016584 | -1.274561 |
| ... | ... | ... | ... | ... | ... | ... |
| 189 | -1.023243 | 0.827082 | 0.695531 | 0.482823 | -0.093190 | -0.130945 |
| 190 | 1.643548 | -0.570770 | 0.545333 | -0.137189 | 0.295910 | -0.891672 |
| 191 | 1.543182 | -0.533850 | 0.979103 | 0.227528 | 0.216491 | -0.016099 |
| 192 | 1.416929 | -1.770555 | 0.592692 | -1.546796 | -0.112419 | -0.017441 |
| 193 | -1.336444 | 0.162214 | -1.528887 | 1.340066 | 0.343647 | -0.060973 |
| 194 | -0.331197 | -0.545328 | 0.449891 | -2.242097 | 0.210220 | 1.299600 |
| 195 | -0.991382 | -0.378373 | -0.215170 | -2.818431 | 1.156878 | -0.599042 |
| 196 | 0.827092 | 0.502299 | 0.219306 | 1.474834 | 0.577530 | 0.832676 |
| 197 | 0.976291 | 0.325663 | -0.091820 | 0.723604 | 0.494609 | 0.610596 |
| 198 | 0.903378 | 0.857383 | 0.090549 | 0.948012 | 1.127442 | 0.927032 |
| 199 | -1.135922 | -0.217483 | -0.201444 | 0.204262 | -0.033230 | -0.725561 |
| 200 | -1.143077 | -0.289624 | -0.109440 | 0.093244 | 0.007101 | -0.571608 |
| 201 | -1.325584 | -0.109383 | -0.850284 | -0.442939 | 0.518129 | -0.996845 |
| 202 | 0.270878 | 1.568003 | -0.899682 | 0.187348 | -0.995623 | 0.436835 |
| 203 | -0.010376 | 1.403657 | -0.298654 | 0.126520 | -0.803249 | -0.284875 |
| 204 | -0.149606 | 0.679408 | -0.527828 | 0.145473 | 0.226461 | 0.232361 |
| 205 | -1.281900 | 0.472582 | 2.041397 | -0.186464 | 1.140780 | -0.694445 |
| 206 | -1.561361 | 0.699591 | 0.373931 | 0.512801 | 0.245563 | -1.259098 |
| 207 | -0.548022 | 0.646014 | -0.015758 | -0.364427 | 1.106060 | -0.395692 |
| 208 | -0.689835 | 0.729721 | 0.242422 | 0.167324 | -0.269920 | 0.625568 |
| 209 | -1.182263 | 0.898528 | 0.655331 | 1.146978 | -0.973699 | 0.509883 |
| 210 | -0.465862 | 0.576977 | -0.088421 | 1.290934 | 0.648005 | 0.669298 |
| 211 | -0.265321 | 1.252143 | 0.230904 | 0.383047 | -0.920749 | 0.237760 |
| 212 | 0.205358 | 1.300786 | 0.929349 | -0.432002 | -0.464366 | -0.242135 |
| 213 | -0.025600 | 0.467818 | 0.261063 | -1.437444 | -0.391460 | -0.995280 |
| 214 | -1.082557 | 1.025513 | 2.276661 | 1.056731 | 0.361540 | 1.291351 |
| 215 | -1.297371 | 1.948703 | 2.264684 | 1.377703 | 1.194669 | 1.983124 |
| 216 | -0.926424 | 0.162164 | 1.016687 | 1.945841 | -1.341651 | 0.150826 |
| 217 | -1.375041 | -0.362757 | -0.599873 | 1.478900 | -0.021584 | -0.846072 |
| 218 | -0.974264 | 0.740461 | 0.889462 | 0.014997 | 1.024334 | -0.992000 |
219 rows × 6 columns
In [349]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[349]:
[1314.0, 1103.6617898421102, 933.9046374976435, 830.0952355796812, 752.8157274494505, 696.8283563577859, 641.355058887789, 599.6834692450786, 558.0899857646746, 538.6016435622136, 502.540180641064, 477.03865333096127, 457.1745404655215, 443.08717934712786]
In [350]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[350]:
[<matplotlib.lines.Line2D at 0x1b82c660da0>]
K=3
In [351]:
kmeans_tc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[351]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [352]:
kmeans_tc.labels_
Out[352]:
array([2, 2, 0, 1, 0, 1, 1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2,
0, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,
2, 2, 1, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 2, 2,
0, 0, 0, 0, 1, 0, 2, 2, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 2, 0,
2, 1, 1, 2, 2, 1, 2, 2, 2, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 1, 1, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2,
2, 0, 0, 0, 2, 0, 0, 2, 1, 0, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
0, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1,
1, 0, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2,
2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1])
In [353]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[353]:
array([2, 2, 0, 1, 0, 1, 1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2,
0, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,
2, 2, 1, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 2, 2,
0, 0, 0, 0, 1, 0, 2, 2, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 2, 0,
2, 1, 1, 2, 2, 1, 2, 2, 2, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 1, 1, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2,
2, 0, 0, 0, 2, 0, 0, 2, 1, 0, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
0, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1,
1, 0, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2,
2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1])
In [354]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [355]:
X
Out[355]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | -1.035481 | 1.779354 | 1.874576 | 0.924814 | -0.129662 | 2.608421 | 2 | 0 |
| 1 | 0.965487 | -0.399971 | -1.606069 | 0.008311 | 0.834341 | 0.513694 | 2 | 0 |
| 2 | -0.141249 | -1.969933 | -0.960470 | 1.005123 | -1.117123 | -2.399517 | 0 | 0 |
| 3 | -1.590590 | -0.729741 | -0.575342 | 0.587988 | -0.885561 | -0.752828 | 1 | 0 |
| 4 | -0.391524 | -0.894181 | -0.426309 | 1.017585 | -0.391173 | -0.920259 | 0 | 0 |
| 5 | -1.256622 | -0.886861 | -0.850243 | 0.516749 | -0.491454 | -0.072867 | 1 | 0 |
| 6 | -1.579202 | 0.121365 | -0.522749 | 1.012025 | -0.547676 | -0.140430 | 1 | 0 |
| 7 | -1.760350 | -0.182429 | -0.008789 | 1.576085 | -0.878841 | 0.252104 | 2 | 0 |
| 8 | 1.115526 | 1.555384 | 0.609404 | 0.558809 | -0.514428 | -0.221726 | 2 | 0 |
| 9 | 1.467291 | 1.402697 | 0.806896 | -0.279535 | 0.939735 | 0.758333 | 2 | 0 |
| 10 | 0.972379 | 1.550575 | -0.223468 | 0.899199 | 1.412818 | 0.386724 | 2 | 0 |
| 11 | 0.294385 | 0.890870 | 0.493531 | 0.142145 | 0.212432 | 1.463886 | 2 | 0 |
| 12 | 0.795134 | 0.176458 | 1.588747 | -0.412034 | -0.982878 | -0.299581 | 0 | 0 |
| 13 | 0.694481 | 0.577820 | 0.319393 | 0.451356 | 0.219257 | 0.563199 | 2 | 0 |
| 14 | 1.169114 | 0.075245 | -0.980006 | 1.330732 | 2.094068 | 1.785970 | 2 | 0 |
| 15 | 0.962642 | 0.380225 | -1.261850 | 1.044019 | 1.339949 | 1.776870 | 2 | 0 |
| 16 | 1.352245 | 0.463507 | -0.679184 | 0.941519 | 2.196602 | 1.991369 | 2 | 0 |
| 17 | 1.784002 | -1.453636 | -1.128885 | -0.626496 | 0.399672 | -0.605861 | 0 | 0 |
| 18 | 0.929212 | -0.538274 | -1.016394 | -0.167176 | 0.557933 | 1.511009 | 2 | 0 |
| 19 | 1.199761 | -0.727252 | 0.322239 | -1.105069 | -0.125311 | -0.979170 | 0 | 0 |
| 20 | -0.485056 | 0.796900 | 0.581966 | 1.884586 | -0.705890 | -0.725300 | 2 | 0 |
| 21 | -0.547233 | 0.692440 | -0.162284 | 2.025268 | -0.631876 | -0.337240 | 2 | 0 |
| 22 | 1.446103 | -0.074850 | -0.132752 | -0.064117 | -0.209506 | -0.465551 | 0 | 0 |
| 23 | -0.312063 | 0.030270 | -1.160963 | 0.726155 | -1.511552 | -0.509175 | 2 | 0 |
| 24 | 1.175126 | -0.143713 | -0.522479 | 0.641015 | 0.500311 | 0.617748 | 2 | 0 |
| 25 | -1.044292 | -0.058933 | -1.340279 | -1.302246 | 1.751828 | -0.815403 | 1 | 0 |
| 26 | -0.849044 | 0.079838 | -0.400536 | -1.312330 | 1.498217 | -0.550869 | 1 | 0 |
| 27 | -0.730672 | -0.326196 | -0.478608 | -0.832610 | -0.556236 | -0.653280 | 1 | 0 |
| 28 | -0.380922 | -0.892886 | -0.555313 | -0.113628 | 1.211258 | -0.901155 | 1 | 0 |
| 29 | -0.368302 | -1.168844 | -0.094765 | -0.158075 | 1.016584 | -1.274561 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -1.023243 | 0.827082 | 0.695531 | 0.482823 | -0.093190 | -0.130945 | 2 | 1 |
| 190 | 1.643548 | -0.570770 | 0.545333 | -0.137189 | 0.295910 | -0.891672 | 0 | 1 |
| 191 | 1.543182 | -0.533850 | 0.979103 | 0.227528 | 0.216491 | -0.016099 | 0 | 1 |
| 192 | 1.416929 | -1.770555 | 0.592692 | -1.546796 | -0.112419 | -0.017441 | 0 | 1 |
| 193 | -1.336444 | 0.162214 | -1.528887 | 1.340066 | 0.343647 | -0.060973 | 1 | 1 |
| 194 | -0.331197 | -0.545328 | 0.449891 | -2.242097 | 0.210220 | 1.299600 | 0 | 1 |
| 195 | -0.991382 | -0.378373 | -0.215170 | -2.818431 | 1.156878 | -0.599042 | 1 | 1 |
| 196 | 0.827092 | 0.502299 | 0.219306 | 1.474834 | 0.577530 | 0.832676 | 2 | 1 |
| 197 | 0.976291 | 0.325663 | -0.091820 | 0.723604 | 0.494609 | 0.610596 | 2 | 1 |
| 198 | 0.903378 | 0.857383 | 0.090549 | 0.948012 | 1.127442 | 0.927032 | 2 | 1 |
| 199 | -1.135922 | -0.217483 | -0.201444 | 0.204262 | -0.033230 | -0.725561 | 1 | 1 |
| 200 | -1.143077 | -0.289624 | -0.109440 | 0.093244 | 0.007101 | -0.571608 | 1 | 1 |
| 201 | -1.325584 | -0.109383 | -0.850284 | -0.442939 | 0.518129 | -0.996845 | 1 | 1 |
| 202 | 0.270878 | 1.568003 | -0.899682 | 0.187348 | -0.995623 | 0.436835 | 2 | 1 |
| 203 | -0.010376 | 1.403657 | -0.298654 | 0.126520 | -0.803249 | -0.284875 | 2 | 1 |
| 204 | -0.149606 | 0.679408 | -0.527828 | 0.145473 | 0.226461 | 0.232361 | 2 | 1 |
| 205 | -1.281900 | 0.472582 | 2.041397 | -0.186464 | 1.140780 | -0.694445 | 1 | 1 |
| 206 | -1.561361 | 0.699591 | 0.373931 | 0.512801 | 0.245563 | -1.259098 | 1 | 1 |
| 207 | -0.548022 | 0.646014 | -0.015758 | -0.364427 | 1.106060 | -0.395692 | 1 | 1 |
| 208 | -0.689835 | 0.729721 | 0.242422 | 0.167324 | -0.269920 | 0.625568 | 2 | 1 |
| 209 | -1.182263 | 0.898528 | 0.655331 | 1.146978 | -0.973699 | 0.509883 | 2 | 1 |
| 210 | -0.465862 | 0.576977 | -0.088421 | 1.290934 | 0.648005 | 0.669298 | 2 | 1 |
| 211 | -0.265321 | 1.252143 | 0.230904 | 0.383047 | -0.920749 | 0.237760 | 2 | 1 |
| 212 | 0.205358 | 1.300786 | 0.929349 | -0.432002 | -0.464366 | -0.242135 | 2 | 1 |
| 213 | -0.025600 | 0.467818 | 0.261063 | -1.437444 | -0.391460 | -0.995280 | 1 | 1 |
| 214 | -1.082557 | 1.025513 | 2.276661 | 1.056731 | 0.361540 | 1.291351 | 2 | 1 |
| 215 | -1.297371 | 1.948703 | 2.264684 | 1.377703 | 1.194669 | 1.983124 | 2 | 1 |
| 216 | -0.926424 | 0.162164 | 1.016687 | 1.945841 | -1.341651 | 0.150826 | 2 | 1 |
| 217 | -1.375041 | -0.362757 | -0.599873 | 1.478900 | -0.021584 | -0.846072 | 1 | 1 |
| 218 | -0.974264 | 0.740461 | 0.889462 | 0.014997 | 1.024334 | -0.992000 | 1 | 1 |
219 rows × 8 columns
In [356]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[356]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b82c6ae978>
In [357]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[5]))
Urban Place¶
ANN¶
In [234]:
X = df_n_ps_std_tc[5]
In [235]:
y = df_n_ps[5]['chosen']
In [236]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [237]:
X_train.shape
Out[237]:
(162, 6)
In [238]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [239]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [240]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [365]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30, 20, 10), 'learning_rate_init': 0.01, 'max_iter': 300}, que permiten obtener un Accuracy de 77.78% y un Kappa del 47.35
Tiempo total: 27.94 minutos
In [241]:
grid.best_params_={'activation': 'tanh', 'hidden_layer_sizes': (30, 20, 10), 'learning_rate_init': 0.01, 'max_iter': 300}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [242]:
input_tensor = Input(shape = (n0,))
In [243]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [244]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [245]:
model.summary()
Model: "model_12" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_13 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_34 (Dense) (None, 30) 210 _________________________________________________________________ dense_35 (Dense) (None, 20) 620 _________________________________________________________________ dense_36 (Dense) (None, 10) 210 _________________________________________________________________ dense_37 (Dense) (None, 1) 11 ================================================================= Total params: 1,051 Trainable params: 1,051 Non-trainable params: 0 _________________________________________________________________
In [246]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 162 samples, validate on 54 samples Epoch 1/300 162/162 [==============================] - 0s 3ms/step - loss: 0.6994 - accuracy: 0.5370 - val_loss: 0.5857 - val_accuracy: 0.6852 Epoch 2/300 162/162 [==============================] - 0s 121us/step - loss: 0.6278 - accuracy: 0.6914 - val_loss: 0.5539 - val_accuracy: 0.7037 Epoch 3/300 162/162 [==============================] - 0s 117us/step - loss: 0.6073 - accuracy: 0.7099 - val_loss: 0.5637 - val_accuracy: 0.7407 Epoch 4/300 162/162 [==============================] - 0s 117us/step - loss: 0.5976 - accuracy: 0.6852 - val_loss: 0.5488 - val_accuracy: 0.7407 Epoch 5/300 162/162 [==============================] - 0s 111us/step - loss: 0.5667 - accuracy: 0.7037 - val_loss: 0.5419 - val_accuracy: 0.7593 Epoch 6/300 162/162 [==============================] - 0s 142us/step - loss: 0.5646 - accuracy: 0.7284 - val_loss: 0.5491 - val_accuracy: 0.7778 Epoch 7/300 162/162 [==============================] - 0s 111us/step - loss: 0.5514 - accuracy: 0.7284 - val_loss: 0.5498 - val_accuracy: 0.7593 Epoch 8/300 162/162 [==============================] - 0s 117us/step - loss: 0.5258 - accuracy: 0.7346 - val_loss: 0.5469 - val_accuracy: 0.7037 Epoch 9/300 162/162 [==============================] - 0s 142us/step - loss: 0.5067 - accuracy: 0.7284 - val_loss: 0.5490 - val_accuracy: 0.7037 Epoch 10/300 162/162 [==============================] - 0s 123us/step - loss: 0.4963 - accuracy: 0.7469 - val_loss: 0.5583 - val_accuracy: 0.6852 Epoch 11/300 162/162 [==============================] - 0s 136us/step - loss: 0.4891 - accuracy: 0.7469 - val_loss: 0.5560 - val_accuracy: 0.7037 Epoch 12/300 162/162 [==============================] - 0s 123us/step - loss: 0.4661 - accuracy: 0.7901 - val_loss: 0.5340 - val_accuracy: 0.6852 Epoch 13/300 162/162 [==============================] - 0s 142us/step - loss: 0.4753 - accuracy: 0.7716 - val_loss: 0.5548 - val_accuracy: 0.7593 Epoch 14/300 162/162 [==============================] - 0s 111us/step - loss: 0.4658 - accuracy: 0.7716 - val_loss: 0.5878 - val_accuracy: 0.6667 Epoch 15/300 162/162 [==============================] - 0s 105us/step - loss: 0.4630 - accuracy: 0.8025 - val_loss: 0.5785 - val_accuracy: 0.7593 Epoch 16/300 162/162 [==============================] - 0s 117us/step - loss: 0.4482 - accuracy: 0.8333 - val_loss: 0.5752 - val_accuracy: 0.7407 Epoch 00016: ReduceLROnPlateau reducing learning rate to 0.004999999888241291. Epoch 17/300 162/162 [==============================] - 0s 142us/step - loss: 0.4313 - accuracy: 0.8580 - val_loss: 0.5693 - val_accuracy: 0.7407 Epoch 18/300 162/162 [==============================] - 0s 130us/step - loss: 0.4242 - accuracy: 0.8519 - val_loss: 0.5612 - val_accuracy: 0.7593 Epoch 19/300 162/162 [==============================] - 0s 123us/step - loss: 0.4176 - accuracy: 0.8519 - val_loss: 0.5413 - val_accuracy: 0.7778 Epoch 20/300 162/162 [==============================] - 0s 111us/step - loss: 0.4122 - accuracy: 0.8580 - val_loss: 0.5434 - val_accuracy: 0.7593 Epoch 21/300 162/162 [==============================] - 0s 117us/step - loss: 0.4078 - accuracy: 0.8148 - val_loss: 0.5756 - val_accuracy: 0.7593 Epoch 22/300 162/162 [==============================] - 0s 117us/step - loss: 0.4034 - accuracy: 0.8272 - val_loss: 0.5847 - val_accuracy: 0.7593 Epoch 23/300 162/162 [==============================] - 0s 123us/step - loss: 0.3991 - accuracy: 0.8272 - val_loss: 0.5888 - val_accuracy: 0.7407 Epoch 24/300 162/162 [==============================] - 0s 136us/step - loss: 0.3897 - accuracy: 0.8395 - val_loss: 0.5933 - val_accuracy: 0.7407 Epoch 25/300 162/162 [==============================] - 0s 136us/step - loss: 0.3781 - accuracy: 0.8395 - val_loss: 0.5867 - val_accuracy: 0.7407 Epoch 26/300 162/162 [==============================] - 0s 123us/step - loss: 0.3710 - accuracy: 0.8457 - val_loss: 0.5836 - val_accuracy: 0.7407 Epoch 00026: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455. Epoch 27/300 162/162 [==============================] - 0s 123us/step - loss: 0.3735 - accuracy: 0.8519 - val_loss: 0.5783 - val_accuracy: 0.7407 Epoch 28/300 162/162 [==============================] - 0s 111us/step - loss: 0.3708 - accuracy: 0.8519 - val_loss: 0.5724 - val_accuracy: 0.7222 Epoch 29/300 162/162 [==============================] - 0s 111us/step - loss: 0.3653 - accuracy: 0.8580 - val_loss: 0.5714 - val_accuracy: 0.7222 Epoch 30/300 162/162 [==============================] - 0s 117us/step - loss: 0.3590 - accuracy: 0.8580 - val_loss: 0.5768 - val_accuracy: 0.7407 Epoch 31/300 162/162 [==============================] - 0s 123us/step - loss: 0.3586 - accuracy: 0.8457 - val_loss: 0.5806 - val_accuracy: 0.7407 Epoch 32/300 162/162 [==============================] - 0s 117us/step - loss: 0.3529 - accuracy: 0.8457 - val_loss: 0.5622 - val_accuracy: 0.7407 Epoch 33/300 162/162 [==============================] - 0s 130us/step - loss: 0.3488 - accuracy: 0.8580 - val_loss: 0.5681 - val_accuracy: 0.7407 Epoch 34/300 162/162 [==============================] - 0s 123us/step - loss: 0.3495 - accuracy: 0.8580 - val_loss: 0.5797 - val_accuracy: 0.7222 Epoch 35/300 162/162 [==============================] - 0s 130us/step - loss: 0.3475 - accuracy: 0.8642 - val_loss: 0.5836 - val_accuracy: 0.7037 Epoch 36/300 162/162 [==============================] - 0s 117us/step - loss: 0.3438 - accuracy: 0.8704 - val_loss: 0.5841 - val_accuracy: 0.7037 Epoch 00036: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228. Epoch 37/300 162/162 [==============================] - 0s 130us/step - loss: 0.3412 - accuracy: 0.8580 - val_loss: 0.5847 - val_accuracy: 0.7222 Epoch 38/300 162/162 [==============================] - 0s 154us/step - loss: 0.3390 - accuracy: 0.8580 - val_loss: 0.5848 - val_accuracy: 0.7037 Epoch 39/300 162/162 [==============================] - 0s 117us/step - loss: 0.3362 - accuracy: 0.8580 - val_loss: 0.5841 - val_accuracy: 0.7037 Epoch 40/300 162/162 [==============================] - 0s 123us/step - loss: 0.3376 - accuracy: 0.8642 - val_loss: 0.5855 - val_accuracy: 0.6852 Epoch 41/300 162/162 [==============================] - 0s 123us/step - loss: 0.3354 - accuracy: 0.8765 - val_loss: 0.5800 - val_accuracy: 0.7222 Epoch 42/300 162/162 [==============================] - 0s 123us/step - loss: 0.3329 - accuracy: 0.8827 - val_loss: 0.5728 - val_accuracy: 0.7407 Epoch 43/300 162/162 [==============================] - 0s 130us/step - loss: 0.3278 - accuracy: 0.8704 - val_loss: 0.5693 - val_accuracy: 0.7222 Epoch 44/300 162/162 [==============================] - 0s 136us/step - loss: 0.3273 - accuracy: 0.8519 - val_loss: 0.5649 - val_accuracy: 0.7222 Epoch 45/300 162/162 [==============================] - 0s 117us/step - loss: 0.3275 - accuracy: 0.8519 - val_loss: 0.5619 - val_accuracy: 0.7222 Epoch 46/300 162/162 [==============================] - 0s 111us/step - loss: 0.3288 - accuracy: 0.8519 - val_loss: 0.5581 - val_accuracy: 0.7407 Epoch 00046: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614. Epoch 47/300 162/162 [==============================] - 0s 117us/step - loss: 0.3272 - accuracy: 0.8704 - val_loss: 0.5590 - val_accuracy: 0.7407 Epoch 48/300 162/162 [==============================] - 0s 117us/step - loss: 0.3253 - accuracy: 0.8642 - val_loss: 0.5601 - val_accuracy: 0.7407 Epoch 49/300 162/162 [==============================] - 0s 136us/step - loss: 0.3240 - accuracy: 0.8642 - val_loss: 0.5606 - val_accuracy: 0.7407 Epoch 50/300 162/162 [==============================] - 0s 136us/step - loss: 0.3226 - accuracy: 0.8642 - val_loss: 0.5603 - val_accuracy: 0.7407 Epoch 51/300 162/162 [==============================] - 0s 123us/step - loss: 0.3212 - accuracy: 0.8704 - val_loss: 0.5588 - val_accuracy: 0.7407 Epoch 52/300 162/162 [==============================] - 0s 130us/step - loss: 0.3204 - accuracy: 0.8704 - val_loss: 0.5586 - val_accuracy: 0.7407 Epoch 53/300 162/162 [==============================] - 0s 123us/step - loss: 0.3192 - accuracy: 0.8765 - val_loss: 0.5590 - val_accuracy: 0.7407 Epoch 54/300 162/162 [==============================] - 0s 123us/step - loss: 0.3185 - accuracy: 0.8704 - val_loss: 0.5602 - val_accuracy: 0.7407 Epoch 55/300 162/162 [==============================] - 0s 130us/step - loss: 0.3179 - accuracy: 0.8765 - val_loss: 0.5596 - val_accuracy: 0.7407 Epoch 56/300 162/162 [==============================] - 0s 117us/step - loss: 0.3171 - accuracy: 0.8827 - val_loss: 0.5607 - val_accuracy: 0.7407 Epoch 00056: ReduceLROnPlateau reducing learning rate to 0.0003124999930150807. Epoch 57/300 162/162 [==============================] - 0s 148us/step - loss: 0.3161 - accuracy: 0.8827 - val_loss: 0.5605 - val_accuracy: 0.7407 Epoch 58/300 162/162 [==============================] - 0s 117us/step - loss: 0.3155 - accuracy: 0.8827 - val_loss: 0.5603 - val_accuracy: 0.7407 Epoch 59/300 162/162 [==============================] - 0s 123us/step - loss: 0.3152 - accuracy: 0.8827 - val_loss: 0.5610 - val_accuracy: 0.7407 Epoch 60/300 162/162 [==============================] - 0s 142us/step - loss: 0.3146 - accuracy: 0.8827 - val_loss: 0.5610 - val_accuracy: 0.7593 Epoch 61/300 162/162 [==============================] - 0s 148us/step - loss: 0.3144 - accuracy: 0.8827 - val_loss: 0.5607 - val_accuracy: 0.7593 Epoch 62/300 162/162 [==============================] - 0s 123us/step - loss: 0.3142 - accuracy: 0.8765 - val_loss: 0.5605 - val_accuracy: 0.7593 Epoch 63/300 162/162 [==============================] - 0s 117us/step - loss: 0.3138 - accuracy: 0.8827 - val_loss: 0.5607 - val_accuracy: 0.7593 Epoch 64/300 162/162 [==============================] - 0s 117us/step - loss: 0.3134 - accuracy: 0.8765 - val_loss: 0.5618 - val_accuracy: 0.7593 Epoch 65/300 162/162 [==============================] - 0s 117us/step - loss: 0.3128 - accuracy: 0.8765 - val_loss: 0.5650 - val_accuracy: 0.7407 Epoch 66/300 162/162 [==============================] - 0s 111us/step - loss: 0.3126 - accuracy: 0.8765 - val_loss: 0.5671 - val_accuracy: 0.7407 Epoch 00066: ReduceLROnPlateau reducing learning rate to 0.00015624999650754035. Epoch 67/300 162/162 [==============================] - 0s 123us/step - loss: 0.3121 - accuracy: 0.8765 - val_loss: 0.5679 - val_accuracy: 0.7407 Epoch 68/300 162/162 [==============================] - 0s 130us/step - loss: 0.3119 - accuracy: 0.8765 - val_loss: 0.5684 - val_accuracy: 0.7407 Epoch 69/300 162/162 [==============================] - 0s 123us/step - loss: 0.3117 - accuracy: 0.8765 - val_loss: 0.5687 - val_accuracy: 0.7407 Epoch 70/300 162/162 [==============================] - 0s 136us/step - loss: 0.3116 - accuracy: 0.8765 - val_loss: 0.5692 - val_accuracy: 0.7407 Epoch 71/300 162/162 [==============================] - 0s 117us/step - loss: 0.3115 - accuracy: 0.8765 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 72/300 162/162 [==============================] - 0s 142us/step - loss: 0.3113 - accuracy: 0.8765 - val_loss: 0.5691 - val_accuracy: 0.7407 Epoch 73/300 162/162 [==============================] - 0s 142us/step - loss: 0.3111 - accuracy: 0.8765 - val_loss: 0.5689 - val_accuracy: 0.7407 Epoch 74/300 162/162 [==============================] - 0s 117us/step - loss: 0.3109 - accuracy: 0.8765 - val_loss: 0.5688 - val_accuracy: 0.7407 Epoch 75/300 162/162 [==============================] - 0s 117us/step - loss: 0.3105 - accuracy: 0.8765 - val_loss: 0.5689 - val_accuracy: 0.7407 Epoch 76/300 162/162 [==============================] - 0s 111us/step - loss: 0.3103 - accuracy: 0.8765 - val_loss: 0.5692 - val_accuracy: 0.7407 Epoch 00076: ReduceLROnPlateau reducing learning rate to 7.812499825377017e-05. Epoch 77/300 162/162 [==============================] - 0s 148us/step - loss: 0.3101 - accuracy: 0.8765 - val_loss: 0.5694 - val_accuracy: 0.7407 Epoch 78/300 162/162 [==============================] - 0s 142us/step - loss: 0.3100 - accuracy: 0.8765 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 79/300 162/162 [==============================] - 0s 136us/step - loss: 0.3099 - accuracy: 0.8765 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 80/300 162/162 [==============================] - 0s 117us/step - loss: 0.3098 - accuracy: 0.8765 - val_loss: 0.5701 - val_accuracy: 0.7407 Epoch 81/300 162/162 [==============================] - 0s 123us/step - loss: 0.3098 - accuracy: 0.8765 - val_loss: 0.5703 - val_accuracy: 0.7407 Epoch 82/300 162/162 [==============================] - 0s 123us/step - loss: 0.3097 - accuracy: 0.8765 - val_loss: 0.5705 - val_accuracy: 0.7407 Epoch 83/300 162/162 [==============================] - 0s 123us/step - loss: 0.3096 - accuracy: 0.8827 - val_loss: 0.5706 - val_accuracy: 0.7407 Epoch 84/300 162/162 [==============================] - 0s 123us/step - loss: 0.3096 - accuracy: 0.8827 - val_loss: 0.5705 - val_accuracy: 0.7407 Epoch 85/300 162/162 [==============================] - 0s 130us/step - loss: 0.3095 - accuracy: 0.8827 - val_loss: 0.5699 - val_accuracy: 0.7407 Epoch 86/300 162/162 [==============================] - 0s 117us/step - loss: 0.3094 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 00086: ReduceLROnPlateau reducing learning rate to 3.9062499126885086e-05. Epoch 87/300 162/162 [==============================] - 0s 117us/step - loss: 0.3093 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 88/300 162/162 [==============================] - 0s 148us/step - loss: 0.3093 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 89/300 162/162 [==============================] - 0s 123us/step - loss: 0.3092 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 90/300 162/162 [==============================] - 0s 142us/step - loss: 0.3092 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 91/300 162/162 [==============================] - 0s 123us/step - loss: 0.3091 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 92/300 162/162 [==============================] - 0s 142us/step - loss: 0.3091 - accuracy: 0.8827 - val_loss: 0.5699 - val_accuracy: 0.7407 Epoch 93/300 162/162 [==============================] - 0s 123us/step - loss: 0.3090 - accuracy: 0.8827 - val_loss: 0.5699 - val_accuracy: 0.7407 Epoch 94/300 162/162 [==============================] - 0s 142us/step - loss: 0.3090 - accuracy: 0.8827 - val_loss: 0.5698 - val_accuracy: 0.7407 Epoch 95/300 162/162 [==============================] - 0s 123us/step - loss: 0.3089 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 96/300 162/162 [==============================] - 0s 111us/step - loss: 0.3089 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 00096: ReduceLROnPlateau reducing learning rate to 1.9531249563442543e-05. Epoch 97/300 162/162 [==============================] - 0s 136us/step - loss: 0.3088 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 98/300 162/162 [==============================] - 0s 123us/step - loss: 0.3088 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 99/300 162/162 [==============================] - 0s 123us/step - loss: 0.3088 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 100/300 162/162 [==============================] - 0s 130us/step - loss: 0.3087 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 101/300 162/162 [==============================] - 0s 123us/step - loss: 0.3087 - accuracy: 0.8827 - val_loss: 0.5698 - val_accuracy: 0.7407 Epoch 102/300 162/162 [==============================] - 0s 117us/step - loss: 0.3087 - accuracy: 0.8827 - val_loss: 0.5698 - val_accuracy: 0.7407 Epoch 103/300 162/162 [==============================] - 0s 117us/step - loss: 0.3087 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 104/300 162/162 [==============================] - 0s 136us/step - loss: 0.3087 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 105/300 162/162 [==============================] - 0s 130us/step - loss: 0.3086 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 106/300 162/162 [==============================] - 0s 142us/step - loss: 0.3086 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 00106: ReduceLROnPlateau reducing learning rate to 9.765624781721272e-06. Epoch 107/300 162/162 [==============================] - 0s 130us/step - loss: 0.3086 - accuracy: 0.8827 - val_loss: 0.5697 - val_accuracy: 0.7407 Epoch 108/300 162/162 [==============================] - 0s 117us/step - loss: 0.3086 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 109/300 162/162 [==============================] - 0s 111us/step - loss: 0.3086 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 110/300 162/162 [==============================] - 0s 111us/step - loss: 0.3085 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 111/300 162/162 [==============================] - 0s 130us/step - loss: 0.3085 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 112/300 162/162 [==============================] - 0s 130us/step - loss: 0.3085 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 113/300 162/162 [==============================] - 0s 123us/step - loss: 0.3085 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 114/300 162/162 [==============================] - 0s 117us/step - loss: 0.3085 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 115/300 162/162 [==============================] - 0s 117us/step - loss: 0.3085 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 116/300 162/162 [==============================] - 0s 148us/step - loss: 0.3085 - accuracy: 0.8827 - val_loss: 0.5694 - val_accuracy: 0.7407 Epoch 00116: ReduceLROnPlateau reducing learning rate to 4.882812390860636e-06. Epoch 117/300 162/162 [==============================] - 0s 136us/step - loss: 0.3085 - accuracy: 0.8827 - val_loss: 0.5694 - val_accuracy: 0.7407 Epoch 118/300 162/162 [==============================] - 0s 136us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5694 - val_accuracy: 0.7407 Epoch 119/300 162/162 [==============================] - 0s 117us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5694 - val_accuracy: 0.7407 Epoch 120/300 162/162 [==============================] - 0s 123us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5694 - val_accuracy: 0.7407 Epoch 121/300 162/162 [==============================] - 0s 111us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5694 - val_accuracy: 0.7407 Epoch 122/300 162/162 [==============================] - 0s 160us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 123/300 162/162 [==============================] - 0s 130us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 124/300 162/162 [==============================] - 0s 130us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 125/300 162/162 [==============================] - 0s 123us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 126/300 162/162 [==============================] - 0s 123us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00126: ReduceLROnPlateau reducing learning rate to 2.441406195430318e-06. Epoch 127/300 162/162 [==============================] - 0s 123us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 128/300 162/162 [==============================] - 0s 117us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 129/300 162/162 [==============================] - 0s 136us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 130/300 162/162 [==============================] - 0s 111us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 131/300 162/162 [==============================] - 0s 123us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 132/300 162/162 [==============================] - 0s 117us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 133/300 162/162 [==============================] - 0s 136us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 134/300 162/162 [==============================] - 0s 105us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 135/300 162/162 [==============================] - 0s 130us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 136/300 162/162 [==============================] - 0s 123us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 00136: ReduceLROnPlateau reducing learning rate to 1.220703097715159e-06. Epoch 137/300 162/162 [==============================] - 0s 130us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 138/300 162/162 [==============================] - 0s 136us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 139/300 162/162 [==============================] - 0s 130us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 140/300 162/162 [==============================] - 0s 111us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 141/300 162/162 [==============================] - 0s 111us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 142/300 162/162 [==============================] - 0s 136us/step - loss: 0.3084 - accuracy: 0.8827 - val_loss: 0.5696 - val_accuracy: 0.7407 Epoch 143/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 144/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 145/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 146/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00146: ReduceLROnPlateau reducing learning rate to 6.103515488575795e-07. Epoch 147/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 148/300 162/162 [==============================] - 0s 148us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 149/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 150/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 151/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 152/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 153/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 154/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 155/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 156/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00156: ReduceLROnPlateau reducing learning rate to 3.0517577442878974e-07. Epoch 157/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 158/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 159/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 160/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 161/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 162/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 163/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 164/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 165/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 166/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00166: ReduceLROnPlateau reducing learning rate to 1.5258788721439487e-07. Epoch 167/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 168/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 169/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 170/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 171/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 172/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 173/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 174/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 175/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 176/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00176: ReduceLROnPlateau reducing learning rate to 7.629394360719743e-08. Epoch 177/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 178/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 179/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 180/300 162/162 [==============================] - 0s 142us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 181/300 162/162 [==============================] - 0s 142us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 182/300 162/162 [==============================] - 0s 142us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 183/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 184/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 185/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 186/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00186: ReduceLROnPlateau reducing learning rate to 3.814697180359872e-08. Epoch 187/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 188/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 189/300 162/162 [==============================] - 0s 148us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 190/300 162/162 [==============================] - 0s 142us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 191/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 192/300 162/162 [==============================] - 0s 160us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 193/300 162/162 [==============================] - 0s 160us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 194/300 162/162 [==============================] - 0s 148us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 195/300 162/162 [==============================] - 0s 148us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 196/300 162/162 [==============================] - 0s 148us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00196: ReduceLROnPlateau reducing learning rate to 1.907348590179936e-08. Epoch 197/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 198/300 162/162 [==============================] - 0s 179us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 199/300 162/162 [==============================] - ETA: 0s - loss: 0.3988 - accuracy: 0.75 - 0s 142us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 200/300 162/162 [==============================] - 0s 179us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 201/300 162/162 [==============================] - 0s 154us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 202/300 162/162 [==============================] - 0s 142us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 203/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 204/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 205/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 206/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00206: ReduceLROnPlateau reducing learning rate to 9.53674295089968e-09. Epoch 207/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 208/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 209/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 210/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 211/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 212/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 213/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 214/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 215/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 216/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00216: ReduceLROnPlateau reducing learning rate to 4.76837147544984e-09. Epoch 217/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 218/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 219/300 162/162 [==============================] - 0s 99us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 220/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 221/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 222/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 223/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 224/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 225/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 226/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00226: ReduceLROnPlateau reducing learning rate to 2.38418573772492e-09. Epoch 227/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 228/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 229/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 230/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 231/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 232/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 233/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 234/300 162/162 [==============================] - 0s 154us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 235/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 236/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00236: ReduceLROnPlateau reducing learning rate to 1.19209286886246e-09. Epoch 237/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 238/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 239/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 240/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 241/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 242/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 243/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 244/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 245/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 246/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00246: ReduceLROnPlateau reducing learning rate to 5.9604643443123e-10. Epoch 247/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 248/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 249/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 250/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 251/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 252/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 253/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 254/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 255/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 256/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00256: ReduceLROnPlateau reducing learning rate to 2.98023217215615e-10. Epoch 257/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 258/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 259/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 260/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 261/300 162/162 [==============================] - ETA: 0s - loss: 0.3335 - accuracy: 0.78 - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 262/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 263/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 264/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 265/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 266/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00266: ReduceLROnPlateau reducing learning rate to 1.490116086078075e-10. Epoch 267/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 268/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 269/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 270/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 271/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 272/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 273/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 274/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 275/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 276/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00276: ReduceLROnPlateau reducing learning rate to 7.450580430390374e-11. Epoch 277/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 278/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 279/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 280/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 281/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 282/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 283/300 162/162 [==============================] - 0s 111us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 284/300 162/162 [==============================] - 0s 105us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 285/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 286/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00286: ReduceLROnPlateau reducing learning rate to 3.725290215195187e-11. Epoch 287/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 288/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 289/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 290/300 162/162 [==============================] - 0s 173us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 291/300 162/162 [==============================] - 0s 148us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 292/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 293/300 162/162 [==============================] - 0s 136us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 294/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 295/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 296/300 162/162 [==============================] - 0s 130us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 00296: ReduceLROnPlateau reducing learning rate to 1.8626451075975936e-11. Epoch 297/300 162/162 [==============================] - 0s 117us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 298/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 299/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407 Epoch 300/300 162/162 [==============================] - 0s 123us/step - loss: 0.3083 - accuracy: 0.8827 - val_loss: 0.5695 - val_accuracy: 0.7407
In [247]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 300)
In [248]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
54/54 [==============================] - 0s 93us/step test loss: 0.5695377522044711, test accuracy: 0.7407407164573669
In [249]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.7103174603174603
In [250]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.32978723404255317 [[33 9] [ 5 7]]
KMeans¶
In [376]:
X
Out[376]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 1.228153 | -0.111909 | -0.294563 | -1.482099 | -0.996056 | 0.110925 |
| 1 | -0.401824 | -1.384577 | 0.881209 | 0.754144 | -0.158974 | -1.296081 |
| 2 | -0.336916 | -0.720621 | 0.201084 | 0.348364 | -0.165961 | 0.182389 |
| 3 | -0.787942 | 0.336222 | 0.156622 | -0.619986 | 1.407460 | -1.245124 |
| 4 | -0.881888 | 0.404050 | 0.352235 | -0.615268 | 1.377506 | -0.415796 |
| 5 | -1.125948 | 0.284207 | 0.528151 | 1.033168 | -0.517365 | 0.113763 |
| 6 | 1.864495 | -0.090865 | 0.716920 | -0.761402 | 0.530249 | -0.035863 |
| 7 | 1.240845 | 0.249843 | 0.223925 | -0.408088 | 0.166387 | 0.416687 |
| 8 | 0.875601 | 0.686409 | 0.502905 | 1.097471 | 1.257941 | 0.717023 |
| 9 | 0.394121 | 0.865626 | -1.374420 | 0.173845 | -0.906500 | -0.686859 |
| 10 | 0.751603 | 1.089250 | 0.142673 | 0.667762 | 0.976845 | 1.048915 |
| 11 | 0.799422 | 0.971011 | -0.358468 | 1.110658 | 0.116881 | -0.712692 |
| 12 | -1.418160 | -0.117060 | -0.366533 | 0.038435 | -0.488564 | -1.235111 |
| 13 | -0.071703 | -1.115223 | 1.951631 | 0.497004 | 0.033906 | -0.995556 |
| 14 | -1.278259 | -0.004256 | -0.388294 | 0.419647 | -1.144792 | -0.789842 |
| 15 | -1.348400 | -0.882436 | 0.081470 | -0.422847 | -1.925698 | 0.566488 |
| 16 | -0.987764 | -0.403920 | 1.221277 | -0.674047 | 1.117167 | 0.695178 |
| 17 | 0.282941 | 1.065034 | -0.777297 | -0.167672 | -0.691064 | 0.251171 |
| 18 | 0.002045 | 1.240340 | -0.913457 | -0.608969 | -0.499640 | -0.715666 |
| 19 | 0.177116 | 1.115065 | -2.614488 | -0.670192 | -0.779129 | 0.025307 |
| 20 | -0.182803 | -1.516954 | -0.973426 | 0.548761 | 1.138162 | -0.638997 |
| 21 | -0.170806 | -1.685146 | -0.648144 | -0.354393 | 0.563785 | -0.765276 |
| 22 | -0.119763 | -1.154938 | 1.627973 | 0.128398 | -0.591403 | 0.461470 |
| 23 | -1.077572 | 1.088539 | 0.971213 | 0.103589 | 0.025601 | 1.072686 |
| 24 | -1.019809 | 1.064143 | 0.982876 | -0.044201 | -0.081801 | 0.760251 |
| 25 | -1.096888 | 1.018525 | -0.188419 | -0.477648 | -0.779052 | -0.966832 |
| 26 | -0.429215 | 1.178614 | 1.291963 | 0.764843 | 1.598962 | 0.847178 |
| 27 | -1.200365 | -0.470005 | 0.534275 | 0.383039 | -1.396076 | 0.776101 |
| 28 | 0.871997 | 0.424606 | 1.290033 | -0.011747 | 1.054939 | -0.612275 |
| 29 | 1.244370 | 0.591384 | 1.478840 | -0.554976 | 0.780633 | -0.506044 |
| ... | ... | ... | ... | ... | ... | ... |
| 186 | -0.605024 | 0.939574 | -0.414421 | -0.916940 | 0.885615 | -1.006592 |
| 187 | 1.718038 | -0.200165 | -1.447107 | -0.867399 | -0.686041 | -0.208583 |
| 188 | -0.120106 | 0.965845 | -0.131325 | 1.649784 | 0.831058 | -1.040351 |
| 189 | 1.029001 | 0.908828 | -0.420421 | 1.436201 | 1.417017 | 1.986802 |
| 190 | 1.615723 | 1.026251 | -0.662886 | -0.619579 | 0.492418 | 1.063432 |
| 191 | 1.115026 | 0.462103 | -1.306277 | -0.442027 | -1.399692 | 1.539195 |
| 192 | 0.811902 | 1.116173 | 0.339093 | 0.787392 | 0.009626 | 1.354667 |
| 193 | 1.133130 | 1.175484 | -0.668705 | 0.390358 | -1.546103 | 0.805030 |
| 194 | -0.345830 | -1.335691 | 1.110949 | 0.375368 | -2.301726 | -1.494080 |
| 195 | 1.165388 | 0.181448 | -1.067468 | 1.496216 | 2.009639 | 0.973058 |
| 196 | 1.372897 | -0.178593 | -0.344421 | -2.254259 | -1.937220 | 0.099446 |
| 197 | 0.993975 | -1.109603 | -0.268101 | -0.854584 | -2.134417 | 1.246874 |
| 198 | 0.910787 | -0.689305 | 0.054775 | 0.374695 | -0.609228 | 0.066021 |
| 199 | 0.754410 | 1.007018 | 0.000807 | 2.080566 | 0.611139 | 0.689148 |
| 200 | 1.100934 | 0.748340 | -0.138155 | 2.149923 | 1.024467 | 1.784464 |
| 201 | 0.898432 | 0.993838 | 0.300420 | 2.543541 | 0.818890 | 0.236400 |
| 202 | 2.023069 | 0.128358 | 2.552718 | 0.420028 | 0.484273 | -0.510637 |
| 203 | 0.513160 | -0.063505 | 1.308959 | -0.740080 | 0.374958 | -1.591480 |
| 204 | 0.509622 | 0.239118 | 1.804994 | -0.524125 | 1.121726 | -2.563172 |
| 205 | 1.142153 | 1.127451 | 0.307006 | 1.061001 | 0.536137 | 1.560857 |
| 206 | 0.900240 | 1.251079 | 0.039424 | 1.120889 | 1.056287 | -0.091386 |
| 207 | 0.996446 | 1.228929 | 0.310422 | 1.145966 | 1.092465 | -0.564371 |
| 208 | -1.699954 | 0.922256 | -1.758628 | -0.010227 | 0.880666 | -0.576796 |
| 209 | -1.794948 | 1.077467 | -1.814708 | 0.353202 | 1.329399 | -0.854254 |
| 210 | -1.688284 | 0.750915 | -1.284463 | 0.305007 | 1.571732 | -0.011164 |
| 211 | 0.709608 | 0.695516 | -1.631065 | 1.034398 | -0.362246 | 3.060986 |
| 212 | 0.524603 | 1.211853 | 0.889937 | 1.025018 | 0.400203 | 1.127006 |
| 213 | 0.459928 | -1.312371 | -0.023584 | -0.263364 | -1.095549 | 0.746115 |
| 214 | -0.649891 | 0.216789 | 0.486250 | -0.661054 | 0.414271 | -0.364451 |
| 215 | 0.655230 | -1.285507 | 0.582914 | 0.864601 | -0.599593 | -0.795822 |
216 rows × 6 columns
In [377]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[377]:
[1296.0, 1078.1827047277895, 923.3020026239071, 819.5563896253416, 739.7801417823988, 674.6658680669257, 625.7817512921029, 571.4893390980873, 532.4311978233743, 507.9160125320259, 477.7010786792888, 447.87692991955134, 435.1097377466632, 418.2600781645563]
In [378]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[378]:
[<matplotlib.lines.Line2D at 0x1b82d1ea898>]
K=3
In [379]:
kmeans_tc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[379]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [380]:
kmeans_tc.labels_
Out[380]:
array([0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 1, 1, 1, 0, 1, 2, 1, 0, 1, 0,
1, 1, 1, 1, 2, 1, 1, 1, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
1, 1, 0, 0, 0, 1, 0, 1, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2,
0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 2, 2, 0, 0, 1, 0, 0, 0,
2, 2, 1, 2, 2, 0, 0, 2, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 2, 1,
1, 1, 2, 0, 2, 1, 2, 2, 0, 0, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 2, 1, 1, 0, 2, 1, 0, 2, 1, 2, 2, 1, 1, 2, 0, 0, 2,
0, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 0, 1, 2, 2, 0, 0, 1, 2,
1, 1, 1, 2, 1, 0, 2, 2, 2, 1, 1, 0, 2, 2, 2, 0, 2, 2, 1, 2, 0, 0,
0, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 0, 1, 1])
In [381]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[381]:
array([0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 1, 1, 1, 0, 1, 2, 1, 0, 1, 0,
1, 1, 1, 1, 2, 1, 1, 1, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
1, 1, 0, 0, 0, 1, 0, 1, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2,
0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 2, 2, 0, 0, 1, 0, 0, 0,
2, 2, 1, 2, 2, 0, 0, 2, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 2, 1,
1, 1, 2, 0, 2, 1, 2, 2, 0, 0, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 2, 1, 1, 0, 2, 1, 0, 2, 1, 2, 2, 1, 1, 2, 0, 0, 2,
0, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 0, 1, 2, 2, 0, 0, 1, 2,
1, 1, 1, 2, 1, 0, 2, 2, 2, 1, 1, 0, 2, 2, 2, 0, 2, 2, 1, 2, 0, 0,
0, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 0, 1, 1])
In [382]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [383]:
X
Out[383]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1.228153 | -0.111909 | -0.294563 | -1.482099 | -0.996056 | 0.110925 | 0 | 0 |
| 1 | -0.401824 | -1.384577 | 0.881209 | 0.754144 | -0.158974 | -1.296081 | 1 | 0 |
| 2 | -0.336916 | -0.720621 | 0.201084 | 0.348364 | -0.165961 | 0.182389 | 1 | 0 |
| 3 | -0.787942 | 0.336222 | 0.156622 | -0.619986 | 1.407460 | -1.245124 | 1 | 0 |
| 4 | -0.881888 | 0.404050 | 0.352235 | -0.615268 | 1.377506 | -0.415796 | 1 | 0 |
| 5 | -1.125948 | 0.284207 | 0.528151 | 1.033168 | -0.517365 | 0.113763 | 1 | 0 |
| 6 | 1.864495 | -0.090865 | 0.716920 | -0.761402 | 0.530249 | -0.035863 | 0 | 0 |
| 7 | 1.240845 | 0.249843 | 0.223925 | -0.408088 | 0.166387 | 0.416687 | 2 | 0 |
| 8 | 0.875601 | 0.686409 | 0.502905 | 1.097471 | 1.257941 | 0.717023 | 2 | 0 |
| 9 | 0.394121 | 0.865626 | -1.374420 | 0.173845 | -0.906500 | -0.686859 | 2 | 0 |
| 10 | 0.751603 | 1.089250 | 0.142673 | 0.667762 | 0.976845 | 1.048915 | 2 | 0 |
| 11 | 0.799422 | 0.971011 | -0.358468 | 1.110658 | 0.116881 | -0.712692 | 2 | 0 |
| 12 | -1.418160 | -0.117060 | -0.366533 | 0.038435 | -0.488564 | -1.235111 | 1 | 0 |
| 13 | -0.071703 | -1.115223 | 1.951631 | 0.497004 | 0.033906 | -0.995556 | 1 | 0 |
| 14 | -1.278259 | -0.004256 | -0.388294 | 0.419647 | -1.144792 | -0.789842 | 1 | 0 |
| 15 | -1.348400 | -0.882436 | 0.081470 | -0.422847 | -1.925698 | 0.566488 | 0 | 0 |
| 16 | -0.987764 | -0.403920 | 1.221277 | -0.674047 | 1.117167 | 0.695178 | 1 | 0 |
| 17 | 0.282941 | 1.065034 | -0.777297 | -0.167672 | -0.691064 | 0.251171 | 2 | 0 |
| 18 | 0.002045 | 1.240340 | -0.913457 | -0.608969 | -0.499640 | -0.715666 | 1 | 0 |
| 19 | 0.177116 | 1.115065 | -2.614488 | -0.670192 | -0.779129 | 0.025307 | 0 | 0 |
| 20 | -0.182803 | -1.516954 | -0.973426 | 0.548761 | 1.138162 | -0.638997 | 1 | 0 |
| 21 | -0.170806 | -1.685146 | -0.648144 | -0.354393 | 0.563785 | -0.765276 | 0 | 0 |
| 22 | -0.119763 | -1.154938 | 1.627973 | 0.128398 | -0.591403 | 0.461470 | 1 | 0 |
| 23 | -1.077572 | 1.088539 | 0.971213 | 0.103589 | 0.025601 | 1.072686 | 1 | 0 |
| 24 | -1.019809 | 1.064143 | 0.982876 | -0.044201 | -0.081801 | 0.760251 | 1 | 0 |
| 25 | -1.096888 | 1.018525 | -0.188419 | -0.477648 | -0.779052 | -0.966832 | 1 | 0 |
| 26 | -0.429215 | 1.178614 | 1.291963 | 0.764843 | 1.598962 | 0.847178 | 2 | 0 |
| 27 | -1.200365 | -0.470005 | 0.534275 | 0.383039 | -1.396076 | 0.776101 | 1 | 0 |
| 28 | 0.871997 | 0.424606 | 1.290033 | -0.011747 | 1.054939 | -0.612275 | 1 | 0 |
| 29 | 1.244370 | 0.591384 | 1.478840 | -0.554976 | 0.780633 | -0.506044 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | -0.605024 | 0.939574 | -0.414421 | -0.916940 | 0.885615 | -1.006592 | 1 | 1 |
| 187 | 1.718038 | -0.200165 | -1.447107 | -0.867399 | -0.686041 | -0.208583 | 0 | 1 |
| 188 | -0.120106 | 0.965845 | -0.131325 | 1.649784 | 0.831058 | -1.040351 | 2 | 1 |
| 189 | 1.029001 | 0.908828 | -0.420421 | 1.436201 | 1.417017 | 1.986802 | 2 | 1 |
| 190 | 1.615723 | 1.026251 | -0.662886 | -0.619579 | 0.492418 | 1.063432 | 2 | 1 |
| 191 | 1.115026 | 0.462103 | -1.306277 | -0.442027 | -1.399692 | 1.539195 | 0 | 1 |
| 192 | 0.811902 | 1.116173 | 0.339093 | 0.787392 | 0.009626 | 1.354667 | 2 | 1 |
| 193 | 1.133130 | 1.175484 | -0.668705 | 0.390358 | -1.546103 | 0.805030 | 2 | 1 |
| 194 | -0.345830 | -1.335691 | 1.110949 | 0.375368 | -2.301726 | -1.494080 | 1 | 1 |
| 195 | 1.165388 | 0.181448 | -1.067468 | 1.496216 | 2.009639 | 0.973058 | 2 | 1 |
| 196 | 1.372897 | -0.178593 | -0.344421 | -2.254259 | -1.937220 | 0.099446 | 0 | 1 |
| 197 | 0.993975 | -1.109603 | -0.268101 | -0.854584 | -2.134417 | 1.246874 | 0 | 1 |
| 198 | 0.910787 | -0.689305 | 0.054775 | 0.374695 | -0.609228 | 0.066021 | 0 | 1 |
| 199 | 0.754410 | 1.007018 | 0.000807 | 2.080566 | 0.611139 | 0.689148 | 2 | 1 |
| 200 | 1.100934 | 0.748340 | -0.138155 | 2.149923 | 1.024467 | 1.784464 | 2 | 1 |
| 201 | 0.898432 | 0.993838 | 0.300420 | 2.543541 | 0.818890 | 0.236400 | 2 | 1 |
| 202 | 2.023069 | 0.128358 | 2.552718 | 0.420028 | 0.484273 | -0.510637 | 2 | 1 |
| 203 | 0.513160 | -0.063505 | 1.308959 | -0.740080 | 0.374958 | -1.591480 | 1 | 1 |
| 204 | 0.509622 | 0.239118 | 1.804994 | -0.524125 | 1.121726 | -2.563172 | 1 | 1 |
| 205 | 1.142153 | 1.127451 | 0.307006 | 1.061001 | 0.536137 | 1.560857 | 2 | 1 |
| 206 | 0.900240 | 1.251079 | 0.039424 | 1.120889 | 1.056287 | -0.091386 | 2 | 1 |
| 207 | 0.996446 | 1.228929 | 0.310422 | 1.145966 | 1.092465 | -0.564371 | 2 | 1 |
| 208 | -1.699954 | 0.922256 | -1.758628 | -0.010227 | 0.880666 | -0.576796 | 1 | 1 |
| 209 | -1.794948 | 1.077467 | -1.814708 | 0.353202 | 1.329399 | -0.854254 | 1 | 1 |
| 210 | -1.688284 | 0.750915 | -1.284463 | 0.305007 | 1.571732 | -0.011164 | 1 | 1 |
| 211 | 0.709608 | 0.695516 | -1.631065 | 1.034398 | -0.362246 | 3.060986 | 2 | 1 |
| 212 | 0.524603 | 1.211853 | 0.889937 | 1.025018 | 0.400203 | 1.127006 | 2 | 1 |
| 213 | 0.459928 | -1.312371 | -0.023584 | -0.263364 | -1.095549 | 0.746115 | 0 | 1 |
| 214 | -0.649891 | 0.216789 | 0.486250 | -0.661054 | 0.414271 | -0.364451 | 1 | 1 |
| 215 | 0.655230 | -1.285507 | 0.582914 | 0.864601 | -0.599593 | -0.795822 | 1 | 1 |
216 rows × 8 columns
In [384]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[384]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b82d22f630>
Chromagram¶
In [15]:
df_n_ps_std[0].columns
Out[15]:
Index(['durationfiles', 'rmsfiles', 'rmsmedianfiles', 'lowenergyfiles',
'ASRfiles', 'beatspectrumfiles', 'eventdensityfiles', 'tempofiles',
'pulseclarityfiles', 'zerocrossfiles', 'rolloffsfiles',
'brightnessfiles', 'spreadfiles', 'centroidfiles', 'kurtosisfiles',
'flatnessfiles', 'entropyfiles', 'mfccfiles_1', 'mfccfiles_2',
'mfccfiles_3', 'mfccfiles_4', 'mfccfiles_5', 'mfccfiles_6',
'mfccfiles_7', 'mfccfiles_8', 'mfccfiles_9', 'mfccfiles_10',
'mfccfiles_11', 'mfccfiles_12', 'mfccfiles_13', 'inharmonicityfiles',
'bestkeyfiles', 'keyclarityfiles', 'modalityfiles',
'tonalcentroidfiles_1', 'tonalcentroidfiles_2', 'tonalcentroidfiles_3',
'tonalcentroidfiles_4', 'tonalcentroidfiles_5', 'tonalcentroidfiles_6',
'chromagramfiles_1', 'chromagramfiles_2', 'chromagramfiles_3',
'chromagramfiles_4', 'chromagramfiles_5', 'chromagramfiles_6',
'chromagramfiles_7', 'chromagramfiles_8', 'chromagramfiles_9',
'chromagramfiles_10', 'chromagramfiles_11', 'chromagramfiles_12',
'attackslopefiles', 'attackleapfiles', 'chosen'],
dtype='object')
In [16]:
df_n_ps_std[0].columns[40:52]
Out[16]:
Index(['chromagramfiles_1', 'chromagramfiles_2', 'chromagramfiles_3',
'chromagramfiles_4', 'chromagramfiles_5', 'chromagramfiles_6',
'chromagramfiles_7', 'chromagramfiles_8', 'chromagramfiles_9',
'chromagramfiles_10', 'chromagramfiles_11', 'chromagramfiles_12'],
dtype='object')
In [17]:
df_n_ps_std_ch = [None]*len(companies)
for i in range(len(companies)):
df_n_ps_std_ch[i] = pd.DataFrame(df_n_ps_std[i].iloc[:,40:52])
df_n_ps_std_ch[i].columns=df_n_ps_std[i].columns[40:52]
df_n_ps_std_ch[0].info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 315 entries, 0 to 314 Data columns (total 12 columns): chromagramfiles_1 315 non-null float64 chromagramfiles_2 315 non-null float64 chromagramfiles_3 315 non-null float64 chromagramfiles_4 315 non-null float64 chromagramfiles_5 315 non-null float64 chromagramfiles_6 315 non-null float64 chromagramfiles_7 315 non-null float64 chromagramfiles_8 315 non-null float64 chromagramfiles_9 315 non-null float64 chromagramfiles_10 315 non-null float64 chromagramfiles_11 315 non-null float64 chromagramfiles_12 315 non-null float64 dtypes: float64(12) memory usage: 29.6 KB
Arte Francés¶
ANN¶
In [257]:
X = df_n_ps_std_ch[0]
In [258]:
y = df_n_ps[0]['chosen']
In [259]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [260]:
X_train.shape
Out[260]:
(236, 12)
In [261]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [262]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [263]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [395]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30, 30), 'learning_rate_init': 0.003, 'max_iter': 200}, que permiten obtener un Accuracy de 75.85% y un Kappa del 35.14
Tiempo total: 30.13 minutos
In [264]:
grid.best_params_={'activation': 'tanh', 'hidden_layer_sizes': (30, 30), 'learning_rate_init': 0.003, 'max_iter': 200}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [265]:
input_tensor = Input(shape = (n0,))
In [266]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [267]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [268]:
model.summary()
Model: "model_15" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_16 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_43 (Dense) (None, 30) 390 _________________________________________________________________ dense_44 (Dense) (None, 30) 930 _________________________________________________________________ dense_45 (Dense) (None, 1) 31 ================================================================= Total params: 1,351 Trainable params: 1,351 Non-trainable params: 0 _________________________________________________________________
In [269]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 236 samples, validate on 79 samples Epoch 1/200 236/236 [==============================] - 0s 1ms/step - loss: 0.6878 - accuracy: 0.5508 - val_loss: 0.6586 - val_accuracy: 0.6456 Epoch 2/200 236/236 [==============================] - 0s 68us/step - loss: 0.6056 - accuracy: 0.6780 - val_loss: 0.6321 - val_accuracy: 0.6582 Epoch 3/200 236/236 [==============================] - 0s 76us/step - loss: 0.5653 - accuracy: 0.7161 - val_loss: 0.6140 - val_accuracy: 0.7089 Epoch 4/200 236/236 [==============================] - 0s 114us/step - loss: 0.5449 - accuracy: 0.7542 - val_loss: 0.6089 - val_accuracy: 0.7342 Epoch 5/200 236/236 [==============================] - 0s 68us/step - loss: 0.5377 - accuracy: 0.7373 - val_loss: 0.6197 - val_accuracy: 0.7342 Epoch 6/200 236/236 [==============================] - 0s 68us/step - loss: 0.5268 - accuracy: 0.7458 - val_loss: 0.6108 - val_accuracy: 0.7342 Epoch 7/200 236/236 [==============================] - 0s 68us/step - loss: 0.5220 - accuracy: 0.7415 - val_loss: 0.6037 - val_accuracy: 0.7342 Epoch 8/200 236/236 [==============================] - 0s 68us/step - loss: 0.5164 - accuracy: 0.7500 - val_loss: 0.6085 - val_accuracy: 0.7342 Epoch 9/200 236/236 [==============================] - 0s 80us/step - loss: 0.5079 - accuracy: 0.7542 - val_loss: 0.6164 - val_accuracy: 0.7342 Epoch 10/200 236/236 [==============================] - 0s 64us/step - loss: 0.5028 - accuracy: 0.7627 - val_loss: 0.6123 - val_accuracy: 0.7215 Epoch 11/200 236/236 [==============================] - 0s 64us/step - loss: 0.4975 - accuracy: 0.7754 - val_loss: 0.6172 - val_accuracy: 0.7342 Epoch 12/200 236/236 [==============================] - 0s 72us/step - loss: 0.4887 - accuracy: 0.7712 - val_loss: 0.6071 - val_accuracy: 0.7342 Epoch 13/200 236/236 [==============================] - 0s 68us/step - loss: 0.4833 - accuracy: 0.7669 - val_loss: 0.6131 - val_accuracy: 0.7595 Epoch 14/200 236/236 [==============================] - 0s 72us/step - loss: 0.4827 - accuracy: 0.7797 - val_loss: 0.6097 - val_accuracy: 0.7595 Epoch 15/200 236/236 [==============================] - 0s 68us/step - loss: 0.4737 - accuracy: 0.7712 - val_loss: 0.6081 - val_accuracy: 0.7468 Epoch 16/200 236/236 [==============================] - 0s 72us/step - loss: 0.4653 - accuracy: 0.7839 - val_loss: 0.6064 - val_accuracy: 0.7468 Epoch 17/200 236/236 [==============================] - 0s 76us/step - loss: 0.4567 - accuracy: 0.7881 - val_loss: 0.6056 - val_accuracy: 0.7468 Epoch 18/200 236/236 [==============================] - 0s 72us/step - loss: 0.4491 - accuracy: 0.8051 - val_loss: 0.6110 - val_accuracy: 0.7468 Epoch 19/200 236/236 [==============================] - 0s 72us/step - loss: 0.4412 - accuracy: 0.8008 - val_loss: 0.6020 - val_accuracy: 0.7595 Epoch 20/200 236/236 [==============================] - 0s 76us/step - loss: 0.4339 - accuracy: 0.8008 - val_loss: 0.6102 - val_accuracy: 0.7722 Epoch 21/200 236/236 [==============================] - 0s 72us/step - loss: 0.4248 - accuracy: 0.8093 - val_loss: 0.6192 - val_accuracy: 0.7595 Epoch 22/200 236/236 [==============================] - 0s 68us/step - loss: 0.4183 - accuracy: 0.8136 - val_loss: 0.6316 - val_accuracy: 0.7595 Epoch 23/200 236/236 [==============================] - 0s 68us/step - loss: 0.4111 - accuracy: 0.8263 - val_loss: 0.6119 - val_accuracy: 0.7722 Epoch 24/200 236/236 [==============================] - 0s 68us/step - loss: 0.3991 - accuracy: 0.8347 - val_loss: 0.6196 - val_accuracy: 0.7848 Epoch 25/200 236/236 [==============================] - 0s 68us/step - loss: 0.3958 - accuracy: 0.8347 - val_loss: 0.6337 - val_accuracy: 0.7722 Epoch 26/200 236/236 [==============================] - 0s 64us/step - loss: 0.3859 - accuracy: 0.8390 - val_loss: 0.6179 - val_accuracy: 0.7595 Epoch 27/200 236/236 [==============================] - 0s 64us/step - loss: 0.3773 - accuracy: 0.8559 - val_loss: 0.6065 - val_accuracy: 0.7595 Epoch 28/200 236/236 [==============================] - 0s 64us/step - loss: 0.3665 - accuracy: 0.8686 - val_loss: 0.6214 - val_accuracy: 0.7595 Epoch 29/200 236/236 [==============================] - 0s 68us/step - loss: 0.3607 - accuracy: 0.8686 - val_loss: 0.6501 - val_accuracy: 0.7848 Epoch 30/200 236/236 [==============================] - 0s 72us/step - loss: 0.3524 - accuracy: 0.8814 - val_loss: 0.6416 - val_accuracy: 0.7848 Epoch 31/200 236/236 [==============================] - 0s 72us/step - loss: 0.3448 - accuracy: 0.8771 - val_loss: 0.6384 - val_accuracy: 0.7722 Epoch 32/200 236/236 [==============================] - 0s 55us/step - loss: 0.3319 - accuracy: 0.9068 - val_loss: 0.6380 - val_accuracy: 0.7848 Epoch 33/200 236/236 [==============================] - 0s 64us/step - loss: 0.3248 - accuracy: 0.9025 - val_loss: 0.6375 - val_accuracy: 0.7722 Epoch 34/200 236/236 [==============================] - 0s 68us/step - loss: 0.3137 - accuracy: 0.9068 - val_loss: 0.6379 - val_accuracy: 0.7722 Epoch 00034: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 35/200 236/236 [==============================] - 0s 85us/step - loss: 0.3053 - accuracy: 0.9195 - val_loss: 0.6404 - val_accuracy: 0.7722 Epoch 36/200 236/236 [==============================] - 0s 102us/step - loss: 0.3021 - accuracy: 0.9110 - val_loss: 0.6444 - val_accuracy: 0.7722 Epoch 37/200 236/236 [==============================] - 0s 76us/step - loss: 0.2963 - accuracy: 0.9237 - val_loss: 0.6496 - val_accuracy: 0.7722 Epoch 38/200 236/236 [==============================] - 0s 80us/step - loss: 0.2929 - accuracy: 0.9322 - val_loss: 0.6603 - val_accuracy: 0.7722 Epoch 39/200 236/236 [==============================] - 0s 76us/step - loss: 0.2876 - accuracy: 0.9322 - val_loss: 0.6594 - val_accuracy: 0.7722 Epoch 40/200 236/236 [==============================] - 0s 76us/step - loss: 0.2838 - accuracy: 0.9237 - val_loss: 0.6579 - val_accuracy: 0.7722 Epoch 41/200 236/236 [==============================] - 0s 72us/step - loss: 0.2807 - accuracy: 0.9407 - val_loss: 0.6571 - val_accuracy: 0.7848 Epoch 42/200 236/236 [==============================] - 0s 72us/step - loss: 0.2750 - accuracy: 0.9492 - val_loss: 0.6442 - val_accuracy: 0.7848 Epoch 43/200 236/236 [==============================] - 0s 68us/step - loss: 0.2730 - accuracy: 0.9322 - val_loss: 0.6509 - val_accuracy: 0.7722 Epoch 44/200 236/236 [==============================] - 0s 80us/step - loss: 0.2661 - accuracy: 0.9322 - val_loss: 0.6503 - val_accuracy: 0.7848 Epoch 00044: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 45/200 236/236 [==============================] - 0s 68us/step - loss: 0.2609 - accuracy: 0.9407 - val_loss: 0.6535 - val_accuracy: 0.7848 Epoch 46/200 236/236 [==============================] - 0s 72us/step - loss: 0.2597 - accuracy: 0.9407 - val_loss: 0.6632 - val_accuracy: 0.7848 Epoch 47/200 236/236 [==============================] - 0s 68us/step - loss: 0.2568 - accuracy: 0.9407 - val_loss: 0.6629 - val_accuracy: 0.7848 Epoch 48/200 236/236 [==============================] - 0s 72us/step - loss: 0.2549 - accuracy: 0.9449 - val_loss: 0.6638 - val_accuracy: 0.7848 Epoch 49/200 236/236 [==============================] - 0s 85us/step - loss: 0.2530 - accuracy: 0.9449 - val_loss: 0.6635 - val_accuracy: 0.7848 Epoch 50/200 236/236 [==============================] - 0s 80us/step - loss: 0.2506 - accuracy: 0.9492 - val_loss: 0.6610 - val_accuracy: 0.7848 Epoch 51/200 236/236 [==============================] - 0s 76us/step - loss: 0.2487 - accuracy: 0.9449 - val_loss: 0.6626 - val_accuracy: 0.7848 Epoch 52/200 236/236 [==============================] - 0s 72us/step - loss: 0.2470 - accuracy: 0.9449 - val_loss: 0.6553 - val_accuracy: 0.7848 Epoch 53/200 236/236 [==============================] - 0s 72us/step - loss: 0.2456 - accuracy: 0.9534 - val_loss: 0.6596 - val_accuracy: 0.7722 Epoch 54/200 236/236 [==============================] - 0s 72us/step - loss: 0.2437 - accuracy: 0.9534 - val_loss: 0.6595 - val_accuracy: 0.7848 Epoch 00054: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 55/200 236/236 [==============================] - 0s 97us/step - loss: 0.2409 - accuracy: 0.9534 - val_loss: 0.6614 - val_accuracy: 0.7848 Epoch 56/200 236/236 [==============================] - 0s 93us/step - loss: 0.2396 - accuracy: 0.9534 - val_loss: 0.6646 - val_accuracy: 0.7848 Epoch 57/200 236/236 [==============================] - 0s 97us/step - loss: 0.2384 - accuracy: 0.9534 - val_loss: 0.6649 - val_accuracy: 0.7848 Epoch 58/200 236/236 [==============================] - 0s 93us/step - loss: 0.2379 - accuracy: 0.9534 - val_loss: 0.6640 - val_accuracy: 0.7848 Epoch 59/200 236/236 [==============================] - 0s 97us/step - loss: 0.2368 - accuracy: 0.9534 - val_loss: 0.6655 - val_accuracy: 0.7848 Epoch 60/200 236/236 [==============================] - 0s 97us/step - loss: 0.2360 - accuracy: 0.9534 - val_loss: 0.6654 - val_accuracy: 0.7848 Epoch 61/200 236/236 [==============================] - 0s 93us/step - loss: 0.2345 - accuracy: 0.9534 - val_loss: 0.6652 - val_accuracy: 0.7848 Epoch 62/200 236/236 [==============================] - 0s 97us/step - loss: 0.2337 - accuracy: 0.9534 - val_loss: 0.6654 - val_accuracy: 0.7848 Epoch 63/200 236/236 [==============================] - 0s 93us/step - loss: 0.2328 - accuracy: 0.9576 - val_loss: 0.6666 - val_accuracy: 0.7848 Epoch 64/200 236/236 [==============================] - 0s 110us/step - loss: 0.2317 - accuracy: 0.9576 - val_loss: 0.6632 - val_accuracy: 0.7848 Epoch 00064: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 65/200 236/236 [==============================] - 0s 93us/step - loss: 0.2305 - accuracy: 0.9576 - val_loss: 0.6640 - val_accuracy: 0.7848 Epoch 66/200 236/236 [==============================] - 0s 144us/step - loss: 0.2300 - accuracy: 0.9576 - val_loss: 0.6646 - val_accuracy: 0.7848 Epoch 67/200 236/236 [==============================] - 0s 97us/step - loss: 0.2294 - accuracy: 0.9534 - val_loss: 0.6643 - val_accuracy: 0.7848 Epoch 68/200 236/236 [==============================] - 0s 110us/step - loss: 0.2289 - accuracy: 0.9576 - val_loss: 0.6639 - val_accuracy: 0.7848 Epoch 69/200 236/236 [==============================] - 0s 97us/step - loss: 0.2284 - accuracy: 0.9576 - val_loss: 0.6654 - val_accuracy: 0.7848 Epoch 70/200 236/236 [==============================] - 0s 97us/step - loss: 0.2280 - accuracy: 0.9534 - val_loss: 0.6664 - val_accuracy: 0.7722 Epoch 71/200 236/236 [==============================] - 0s 97us/step - loss: 0.2273 - accuracy: 0.9534 - val_loss: 0.6677 - val_accuracy: 0.7722 Epoch 72/200 236/236 [==============================] - 0s 93us/step - loss: 0.2268 - accuracy: 0.9534 - val_loss: 0.6696 - val_accuracy: 0.7722 Epoch 73/200 236/236 [==============================] - 0s 97us/step - loss: 0.2265 - accuracy: 0.9534 - val_loss: 0.6711 - val_accuracy: 0.7722 Epoch 74/200 236/236 [==============================] - 0s 89us/step - loss: 0.2258 - accuracy: 0.9534 - val_loss: 0.6716 - val_accuracy: 0.7848 Epoch 00074: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 75/200 236/236 [==============================] - 0s 97us/step - loss: 0.2253 - accuracy: 0.9576 - val_loss: 0.6726 - val_accuracy: 0.7848 Epoch 76/200 236/236 [==============================] - 0s 97us/step - loss: 0.2251 - accuracy: 0.9576 - val_loss: 0.6724 - val_accuracy: 0.7848 Epoch 77/200 236/236 [==============================] - 0s 97us/step - loss: 0.2249 - accuracy: 0.9576 - val_loss: 0.6721 - val_accuracy: 0.7848 Epoch 78/200 236/236 [==============================] - 0s 97us/step - loss: 0.2246 - accuracy: 0.9576 - val_loss: 0.6725 - val_accuracy: 0.7848 Epoch 79/200 236/236 [==============================] - 0s 102us/step - loss: 0.2244 - accuracy: 0.9576 - val_loss: 0.6729 - val_accuracy: 0.7848 Epoch 80/200 236/236 [==============================] - 0s 110us/step - loss: 0.2242 - accuracy: 0.9576 - val_loss: 0.6735 - val_accuracy: 0.7848 Epoch 81/200 236/236 [==============================] - 0s 97us/step - loss: 0.2239 - accuracy: 0.9576 - val_loss: 0.6747 - val_accuracy: 0.7848 Epoch 82/200 236/236 [==============================] - 0s 93us/step - loss: 0.2237 - accuracy: 0.9576 - val_loss: 0.6743 - val_accuracy: 0.7848 Epoch 83/200 236/236 [==============================] - 0s 85us/step - loss: 0.2235 - accuracy: 0.9576 - val_loss: 0.6750 - val_accuracy: 0.7848 Epoch 84/200 236/236 [==============================] - 0s 97us/step - loss: 0.2232 - accuracy: 0.9576 - val_loss: 0.6752 - val_accuracy: 0.7848 Epoch 00084: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 85/200 236/236 [==============================] - 0s 93us/step - loss: 0.2229 - accuracy: 0.9576 - val_loss: 0.6753 - val_accuracy: 0.7848 Epoch 86/200 236/236 [==============================] - 0s 102us/step - loss: 0.2229 - accuracy: 0.9576 - val_loss: 0.6756 - val_accuracy: 0.7848 Epoch 87/200 236/236 [==============================] - 0s 89us/step - loss: 0.2227 - accuracy: 0.9576 - val_loss: 0.6755 - val_accuracy: 0.7848 Epoch 88/200 236/236 [==============================] - 0s 106us/step - loss: 0.2226 - accuracy: 0.9576 - val_loss: 0.6756 - val_accuracy: 0.7848 Epoch 89/200 236/236 [==============================] - 0s 93us/step - loss: 0.2225 - accuracy: 0.9576 - val_loss: 0.6758 - val_accuracy: 0.7848 Epoch 90/200 236/236 [==============================] - 0s 97us/step - loss: 0.2224 - accuracy: 0.9576 - val_loss: 0.6754 - val_accuracy: 0.7848 Epoch 91/200 236/236 [==============================] - 0s 89us/step - loss: 0.2223 - accuracy: 0.9576 - val_loss: 0.6754 - val_accuracy: 0.7848 Epoch 92/200 236/236 [==============================] - 0s 93us/step - loss: 0.2221 - accuracy: 0.9576 - val_loss: 0.6756 - val_accuracy: 0.7848 Epoch 93/200 236/236 [==============================] - 0s 85us/step - loss: 0.2221 - accuracy: 0.9576 - val_loss: 0.6755 - val_accuracy: 0.7848 Epoch 94/200 236/236 [==============================] - 0s 102us/step - loss: 0.2219 - accuracy: 0.9576 - val_loss: 0.6755 - val_accuracy: 0.7848 Epoch 00094: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 95/200 236/236 [==============================] - 0s 114us/step - loss: 0.2217 - accuracy: 0.9576 - val_loss: 0.6755 - val_accuracy: 0.7848 Epoch 96/200 236/236 [==============================] - 0s 106us/step - loss: 0.2217 - accuracy: 0.9576 - val_loss: 0.6755 - val_accuracy: 0.7848 Epoch 97/200 236/236 [==============================] - 0s 93us/step - loss: 0.2216 - accuracy: 0.9576 - val_loss: 0.6757 - val_accuracy: 0.7848 Epoch 98/200 236/236 [==============================] - 0s 106us/step - loss: 0.2215 - accuracy: 0.9576 - val_loss: 0.6757 - val_accuracy: 0.7848 Epoch 99/200 236/236 [==============================] - 0s 102us/step - loss: 0.2215 - accuracy: 0.9576 - val_loss: 0.6757 - val_accuracy: 0.7848 Epoch 100/200 236/236 [==============================] - 0s 89us/step - loss: 0.2214 - accuracy: 0.9576 - val_loss: 0.6759 - val_accuracy: 0.7848 Epoch 101/200 236/236 [==============================] - 0s 106us/step - loss: 0.2214 - accuracy: 0.9576 - val_loss: 0.6760 - val_accuracy: 0.7848 Epoch 102/200 236/236 [==============================] - 0s 93us/step - loss: 0.2213 - accuracy: 0.9576 - val_loss: 0.6760 - val_accuracy: 0.7848 Epoch 103/200 236/236 [==============================] - 0s 93us/step - loss: 0.2212 - accuracy: 0.9576 - val_loss: 0.6760 - val_accuracy: 0.7848 Epoch 104/200 236/236 [==============================] - 0s 110us/step - loss: 0.2212 - accuracy: 0.9576 - val_loss: 0.6763 - val_accuracy: 0.7848 Epoch 00104: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 105/200 236/236 [==============================] - 0s 102us/step - loss: 0.2211 - accuracy: 0.9576 - val_loss: 0.6764 - val_accuracy: 0.7848 Epoch 106/200 236/236 [==============================] - 0s 106us/step - loss: 0.2211 - accuracy: 0.9576 - val_loss: 0.6765 - val_accuracy: 0.7848 Epoch 107/200 236/236 [==============================] - 0s 110us/step - loss: 0.2211 - accuracy: 0.9576 - val_loss: 0.6765 - val_accuracy: 0.7848 Epoch 108/200 236/236 [==============================] - 0s 97us/step - loss: 0.2210 - accuracy: 0.9576 - val_loss: 0.6765 - val_accuracy: 0.7848 Epoch 109/200 236/236 [==============================] - 0s 97us/step - loss: 0.2210 - accuracy: 0.9576 - val_loss: 0.6765 - val_accuracy: 0.7848 Epoch 110/200 236/236 [==============================] - 0s 102us/step - loss: 0.2210 - accuracy: 0.9576 - val_loss: 0.6765 - val_accuracy: 0.7848 Epoch 111/200 236/236 [==============================] - 0s 106us/step - loss: 0.2209 - accuracy: 0.9576 - val_loss: 0.6765 - val_accuracy: 0.7848 Epoch 112/200 236/236 [==============================] - 0s 93us/step - loss: 0.2209 - accuracy: 0.9576 - val_loss: 0.6765 - val_accuracy: 0.7848 Epoch 113/200 236/236 [==============================] - 0s 106us/step - loss: 0.2209 - accuracy: 0.9576 - val_loss: 0.6765 - val_accuracy: 0.7848 Epoch 114/200 236/236 [==============================] - 0s 89us/step - loss: 0.2208 - accuracy: 0.9576 - val_loss: 0.6764 - val_accuracy: 0.7848 Epoch 00114: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 115/200 236/236 [==============================] - 0s 97us/step - loss: 0.2208 - accuracy: 0.9576 - val_loss: 0.6764 - val_accuracy: 0.7848 Epoch 116/200 236/236 [==============================] - 0s 89us/step - loss: 0.2208 - accuracy: 0.9576 - val_loss: 0.6764 - val_accuracy: 0.7848 Epoch 117/200 236/236 [==============================] - 0s 102us/step - loss: 0.2208 - accuracy: 0.9576 - val_loss: 0.6764 - val_accuracy: 0.7848 Epoch 118/200 236/236 [==============================] - 0s 93us/step - loss: 0.2208 - accuracy: 0.9576 - val_loss: 0.6764 - val_accuracy: 0.7848 Epoch 119/200 236/236 [==============================] - 0s 119us/step - loss: 0.2207 - accuracy: 0.9576 - val_loss: 0.6763 - val_accuracy: 0.7848 Epoch 120/200 236/236 [==============================] - 0s 114us/step - loss: 0.2207 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 121/200 236/236 [==============================] - 0s 110us/step - loss: 0.2207 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 122/200 236/236 [==============================] - 0s 106us/step - loss: 0.2207 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 123/200 236/236 [==============================] - 0s 97us/step - loss: 0.2207 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 124/200 236/236 [==============================] - 0s 93us/step - loss: 0.2207 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 00124: ReduceLROnPlateau reducing learning rate to 2.9296875254658516e-06. Epoch 125/200 236/236 [==============================] - 0s 93us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 126/200 236/236 [==============================] - 0s 89us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 127/200 236/236 [==============================] - 0s 97us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 128/200 236/236 [==============================] - 0s 93us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 129/200 236/236 [==============================] - 0s 102us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 130/200 236/236 [==============================] - 0s 93us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 131/200 236/236 [==============================] - 0s 106us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 132/200 236/236 [==============================] - 0s 97us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 133/200 236/236 [==============================] - 0s 93us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 134/200 236/236 [==============================] - 0s 89us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 00134: ReduceLROnPlateau reducing learning rate to 1.4648437627329258e-06. Epoch 135/200 236/236 [==============================] - 0s 93us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 136/200 236/236 [==============================] - 0s 97us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 137/200 236/236 [==============================] - 0s 89us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 138/200 236/236 [==============================] - 0s 97us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 139/200 236/236 [==============================] - 0s 89us/step - loss: 0.2206 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 140/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 141/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 142/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 143/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 144/200 236/236 [==============================] - 0s 97us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 00144: ReduceLROnPlateau reducing learning rate to 7.324218813664629e-07. Epoch 145/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 146/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 147/200 236/236 [==============================] - 0s 85us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 148/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 149/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 150/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 151/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 152/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 153/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 154/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 00154: ReduceLROnPlateau reducing learning rate to 3.6621094068323146e-07. Epoch 155/200 236/236 [==============================] - 0s 97us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 156/200 236/236 [==============================] - 0s 106us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 157/200 236/236 [==============================] - 0s 106us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 158/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 159/200 236/236 [==============================] - 0s 85us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 160/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 161/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 162/200 236/236 [==============================] - 0s 97us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 163/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 164/200 236/236 [==============================] - 0s 97us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 00164: ReduceLROnPlateau reducing learning rate to 1.8310547034161573e-07. Epoch 165/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 166/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 167/200 236/236 [==============================] - 0s 85us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 168/200 236/236 [==============================] - 0s 85us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 169/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 170/200 236/236 [==============================] - 0s 85us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 171/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 172/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 173/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 174/200 236/236 [==============================] - ETA: 0s - loss: 0.2723 - accuracy: 0.96 - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 00174: ReduceLROnPlateau reducing learning rate to 9.155273517080786e-08. Epoch 175/200 236/236 [==============================] - 0s 110us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 176/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 177/200 236/236 [==============================] - 0s 106us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 178/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 179/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 180/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 181/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 182/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 183/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 184/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 00184: ReduceLROnPlateau reducing learning rate to 4.577636758540393e-08. Epoch 185/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 186/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 187/200 236/236 [==============================] - 0s 97us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 188/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 189/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 190/200 236/236 [==============================] - 0s 89us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 191/200 236/236 [==============================] - 0s 106us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 192/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 193/200 236/236 [==============================] - 0s 106us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 194/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 00194: ReduceLROnPlateau reducing learning rate to 2.2888183792701966e-08. Epoch 195/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 196/200 236/236 [==============================] - 0s 106us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 197/200 236/236 [==============================] - 0s 123us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 198/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 199/200 236/236 [==============================] - 0s 102us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848 Epoch 200/200 236/236 [==============================] - 0s 93us/step - loss: 0.2205 - accuracy: 0.9576 - val_loss: 0.6762 - val_accuracy: 0.7848
In [270]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 200)
In [271]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
79/79 [==============================] - 0s 89us/step test loss: 0.6762231539321851, test accuracy: 0.7848101258277893
In [272]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6412151067323482
In [273]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.36138849262957684 [[54 4] [13 8]]
KMeans¶
In [406]:
X
Out[406]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.713160 | 0.038243 | 2.143431 | 0.466379 | 2.480688 | 1.533552 | -0.564788 | -0.236267 | -0.737672 | 0.470152 | -0.935468 | 0.074931 |
| 1 | 1.315247 | 0.387677 | 1.394548 | -0.542923 | -0.194326 | 0.100084 | 0.191568 | 1.691930 | -0.164059 | 0.599296 | -0.175780 | 0.267042 |
| 2 | 1.737505 | -1.440944 | -1.114255 | 0.326790 | -1.157732 | -1.261521 | -1.130503 | 1.585017 | -1.367743 | -1.287569 | -0.805298 | -1.676436 |
| 3 | 1.737506 | -1.127088 | -0.672464 | 0.505231 | -0.830959 | -1.046716 | -0.912922 | 1.082754 | -0.919242 | -0.761758 | -1.023346 | -1.220716 |
| 4 | 1.737506 | 1.455410 | 0.333584 | 1.122628 | 1.139134 | -0.481280 | 0.129896 | 0.915597 | 0.333032 | -0.289806 | -0.236639 | -0.281891 |
| 5 | -1.313694 | -1.631596 | -0.954634 | 2.545293 | -0.841431 | -0.880402 | 1.098767 | 0.874224 | -1.935272 | -1.777115 | -1.259928 | -1.270550 |
| 6 | 0.343081 | -0.554660 | -0.305290 | 0.059946 | -0.331585 | 0.008117 | -0.092598 | 0.086112 | 1.237321 | -0.237146 | 0.239978 | 1.617512 |
| 7 | 1.109261 | -0.479083 | -0.201140 | 1.561772 | -0.314776 | 2.324993 | -0.123793 | 1.681972 | 0.723606 | 1.281028 | 1.332118 | 1.616723 |
| 8 | 1.737506 | 0.431527 | -0.310582 | -0.122085 | -0.401366 | 1.109393 | -0.731551 | -0.284884 | -0.401724 | 0.343534 | 0.512560 | 2.039796 |
| 9 | -1.315956 | -1.270342 | -0.635019 | -0.965063 | -0.677128 | -0.999133 | -0.673936 | 0.377158 | -0.740151 | 0.479661 | 1.359663 | -0.539737 |
| 10 | 1.592092 | 0.167770 | 1.676372 | 0.444937 | 1.198262 | 1.558372 | 0.695811 | 0.857869 | 0.417106 | 1.008119 | 1.359663 | 1.013913 |
| 11 | 1.737506 | 0.061168 | -0.198190 | -0.277339 | -0.315128 | -0.260607 | -0.037409 | 0.713144 | -0.442008 | -0.420659 | -0.067628 | 1.042894 |
| 12 | 1.047772 | 0.511390 | 0.649199 | -0.733636 | -0.679537 | -1.048667 | -0.618650 | -1.049439 | 1.237321 | 0.935768 | 0.609736 | 1.089900 |
| 13 | 0.985207 | 0.757573 | 0.728808 | 0.148076 | -0.402837 | -0.657586 | -0.341424 | 0.352814 | 1.237320 | 0.537897 | -0.314771 | 1.274813 |
| 14 | -1.092408 | 0.724395 | -0.854683 | -1.339781 | -1.038651 | -0.146198 | -1.046078 | -0.968884 | -0.640529 | -0.662849 | 1.359664 | -0.842083 |
| 15 | -0.257098 | -0.953113 | -0.826836 | -1.058296 | -1.143706 | -1.295604 | -0.932057 | 1.298111 | 0.062365 | 0.078659 | 1.359663 | -0.414103 |
| 16 | -1.038548 | -1.285038 | -0.897336 | -0.925038 | -1.321623 | -0.675496 | -1.174686 | -0.220940 | -1.236805 | -0.992367 | 1.359664 | -0.452244 |
| 17 | -0.743373 | -1.006359 | -0.304526 | -0.673197 | 2.468788 | 1.394753 | 2.061863 | -1.147252 | -0.314785 | -0.803420 | -0.543555 | 2.127294 |
| 18 | -0.506076 | 0.745202 | 0.557874 | -0.551711 | 2.482435 | 1.618435 | 3.352869 | -0.896426 | 0.632864 | -0.504095 | -0.396771 | 1.961034 |
| 19 | -0.619836 | -0.900602 | 0.192155 | -0.280406 | 1.602038 | 1.570403 | 3.352869 | -1.028553 | -0.325763 | -0.694475 | -0.448468 | 2.112782 |
| 20 | 0.236496 | 1.024573 | 2.143431 | 2.184352 | -1.309784 | 1.967834 | -1.174015 | -1.738864 | -1.705683 | -1.658302 | 1.034240 | -1.039738 |
| 21 | -0.969823 | 0.186258 | -0.649182 | -0.822502 | -1.173086 | -0.203826 | -1.106955 | -1.095550 | 0.611256 | -1.183571 | 1.359663 | -0.954603 |
| 22 | -0.477230 | -0.147047 | -0.472900 | -0.603956 | -1.185349 | 0.835564 | -0.993918 | -1.280801 | 1.237321 | -0.492384 | 1.208362 | -0.579080 |
| 23 | -0.402673 | -1.613310 | 0.859201 | -1.462753 | -1.304385 | 1.048578 | -1.175049 | -1.320789 | -2.133693 | 0.809224 | 1.359663 | -1.698122 |
| 24 | 1.036252 | -0.180424 | 2.143430 | -0.530143 | -0.581394 | 0.192607 | 0.189292 | 1.455548 | 0.372539 | 1.118521 | -0.352066 | 0.295735 |
| 25 | 1.009571 | -0.114262 | 0.491121 | -0.494523 | -0.560272 | -0.489051 | 0.310555 | -0.762806 | 0.638941 | 1.281027 | -0.377793 | 0.160975 |
| 26 | 1.737505 | -0.691965 | 0.794800 | -0.996395 | -0.859283 | 0.108625 | -0.179964 | -0.404455 | -0.407132 | 0.559305 | 0.490880 | -0.690673 |
| 27 | 0.952878 | -0.600113 | 1.564652 | -0.647102 | -0.354733 | 0.741931 | -0.850897 | -0.165104 | -1.270161 | 1.281028 | 0.689314 | -0.739641 |
| 28 | 0.090687 | -1.135658 | 1.025879 | 0.173394 | 0.141333 | 0.938735 | -0.460724 | 1.033120 | -0.675812 | 1.016227 | 1.359663 | -0.918936 |
| 29 | 1.334056 | 1.036213 | 0.310885 | 1.388872 | 0.536826 | 1.066414 | 2.035180 | 1.368130 | 1.215360 | 1.281028 | 0.571847 | 1.089988 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 285 | -0.818809 | 0.795687 | -0.418744 | -0.981876 | 0.290435 | -1.082166 | 0.916788 | -1.121030 | -0.872233 | -0.467637 | -0.673139 | 2.127294 |
| 286 | -1.350808 | -0.132215 | 1.939439 | -0.255670 | 1.080433 | -0.906077 | -0.076483 | 1.691930 | -1.760598 | -0.831051 | -1.664177 | 0.190914 |
| 287 | -0.268283 | -1.246560 | -0.948278 | -1.092787 | -0.912773 | -1.286685 | -1.170304 | -1.398130 | -0.955533 | 1.281028 | -1.249300 | -1.331567 |
| 288 | -1.499565 | -1.477911 | -1.400613 | -1.454657 | -0.189787 | -1.235822 | -1.124408 | -1.277436 | -1.382225 | 1.281028 | -1.456827 | -1.039202 |
| 289 | -0.316037 | -1.101863 | -0.979251 | -0.947212 | -0.382959 | -1.211602 | -1.164073 | -0.572011 | -0.586600 | 1.281028 | -1.035303 | -1.071429 |
| 290 | -1.429079 | -0.014357 | -0.502745 | -0.434442 | 2.818635 | 1.078430 | 2.198959 | -1.053548 | -2.088193 | -1.732987 | -1.761028 | -0.159791 |
| 291 | 0.142459 | 0.454640 | -1.378454 | 1.070823 | -1.223040 | 2.764417 | 1.542144 | -1.706716 | 0.232404 | -1.955043 | 0.868232 | -1.674045 |
| 292 | 0.369579 | 0.015402 | -0.629733 | 2.545293 | -0.222337 | 1.917223 | 0.555192 | -1.194461 | 0.161682 | -1.235773 | 0.462188 | -0.512327 |
| 293 | 1.737506 | -0.048004 | 1.588206 | -0.467110 | 0.510737 | 0.554321 | 0.532244 | 1.152385 | -0.138238 | 0.923415 | -0.494020 | 0.305075 |
| 294 | -0.072827 | -0.808718 | 2.143431 | -1.069392 | 0.333942 | 1.075808 | -0.574391 | 0.052452 | -1.091144 | 1.209060 | -1.203635 | -0.631894 |
| 295 | 1.096294 | -0.192606 | 0.838680 | -0.968919 | 0.814071 | 1.951969 | -0.304336 | -0.213012 | -0.860743 | 1.281028 | -0.964629 | -0.039173 |
| 296 | 1.197861 | -0.481339 | -0.039784 | 0.576523 | 0.719339 | 1.230426 | -0.013807 | 0.576144 | 1.237321 | 0.827248 | 0.863710 | 1.347974 |
| 297 | 0.831369 | -0.845061 | -0.412444 | -0.202774 | 0.499287 | 0.798141 | 1.143779 | 0.135898 | 0.903542 | 1.103870 | 1.359663 | 1.309758 |
| 298 | 1.129174 | -0.778390 | -0.347478 | 0.028754 | 0.896022 | 1.021079 | 1.609960 | 0.261949 | 0.607068 | 1.111758 | 1.359663 | 1.623317 |
| 299 | 1.235661 | 0.246339 | -0.055182 | -0.274201 | -0.453087 | -0.448112 | -0.693517 | -0.103535 | 1.237321 | 0.849533 | 0.778756 | 0.601407 |
| 300 | 1.617641 | 0.157580 | 0.320652 | -0.272952 | 0.399767 | 0.391633 | -0.493105 | 0.638211 | 0.910413 | 1.281028 | 0.611215 | 0.781573 |
| 301 | 0.904215 | -0.238528 | 0.650660 | -0.588166 | 0.058915 | -0.260010 | -0.734161 | 0.076079 | 0.334341 | 1.281028 | -0.009255 | 0.453380 |
| 302 | -1.571329 | 2.288385 | -0.858389 | 1.605747 | 0.877803 | -1.392311 | -0.299009 | -1.732948 | 0.377403 | -1.348885 | -1.797949 | 0.161259 |
| 303 | -1.111497 | 1.012712 | -0.373804 | -0.488606 | -0.534590 | -1.065890 | -0.687799 | -1.079827 | 1.237321 | -0.872941 | -1.357570 | -1.172663 |
| 304 | -0.632774 | 2.288385 | 1.193051 | 0.583811 | 1.042355 | -0.849096 | 1.848927 | -1.561165 | 0.223166 | -0.146801 | -1.172130 | -0.154950 |
| 305 | -1.675063 | 0.049104 | -0.121444 | -1.322550 | -0.298216 | -1.383538 | 3.352869 | -0.922058 | -2.012971 | -0.306843 | -1.798988 | 0.307357 |
| 306 | -1.675063 | -0.302934 | 1.841519 | -1.463728 | 0.044986 | -1.392689 | 2.251379 | 0.179143 | -2.131740 | 1.281028 | -1.476204 | 1.119567 |
| 307 | -1.675063 | -0.345332 | 0.759301 | -1.463728 | -0.944610 | -1.392689 | 2.839571 | -1.657298 | -2.134915 | 1.281027 | -1.798988 | -0.818327 |
| 308 | -0.701240 | -0.718707 | 2.143431 | -0.317960 | 0.753769 | 0.883849 | 0.080036 | 0.067906 | -1.106120 | 0.663348 | -0.295994 | -0.620494 |
| 309 | 0.283947 | -0.770573 | 2.143431 | -0.677678 | 0.254128 | 1.306505 | -0.471240 | 1.183162 | -1.285117 | 0.322375 | 0.125126 | -0.725452 |
| 310 | -0.684797 | -0.424879 | 1.965265 | 0.285506 | 1.654633 | 1.714850 | 0.379435 | 0.747071 | -0.436120 | 1.281028 | 0.814894 | -0.455133 |
| 311 | -0.063614 | 0.387844 | 1.083088 | -0.000037 | 0.092909 | 0.624221 | 0.267615 | 1.377100 | 1.237321 | 0.720428 | 0.122085 | 0.422431 |
| 312 | 1.272215 | 0.276107 | 2.143431 | -0.015484 | 1.545799 | 1.751362 | 0.646849 | 1.047746 | 0.159537 | 0.294111 | -0.397554 | 0.554605 |
| 313 | -1.349102 | 0.408011 | -0.212504 | 2.027495 | 1.056566 | -0.074589 | 0.348765 | 1.691929 | 1.002332 | 0.337931 | 0.708780 | -0.469953 |
| 314 | 0.377466 | -0.342265 | -0.366249 | -0.135576 | -0.759547 | 1.018877 | -0.961553 | 1.691929 | 0.534889 | 0.262599 | -0.220736 | 0.127432 |
315 rows × 12 columns
In [407]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[407]:
[3780.0, 3195.1235725667257, 2811.651587927651, 2629.426866311046, 2498.0440346782034, 2372.504055975522, 2279.167916120524, 2172.592510139712, 2092.9155365617485, 2035.7890606826481, 1937.9162920732826, 1905.3053295274888, 1820.7622703061425, 1785.824822903035]
In [408]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[408]:
[<matplotlib.lines.Line2D at 0x1b82d788048>]
K=3
In [409]:
kmeans_ch = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[409]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [410]:
kmeans_ch.labels_
Out[410]:
array([1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0,
0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 2, 0, 2, 0, 2, 1, 1, 1, 0, 0, 1,
0, 0, 0, 0, 1, 2, 2, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 2, 2, 2, 0,
1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2,
2, 1, 0, 1, 0, 2, 2, 2, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,
1, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 1, 1, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 2, 2, 2, 1, 1, 0, 0, 1,
1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 2, 0, 0, 0,
0, 0, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 0, 0, 0,
1, 1, 2, 2, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 0, 1, 1, 0, 0,
1, 1, 1, 0, 1, 1, 2, 2, 0, 1, 1, 0, 1, 1, 1, 0, 1, 2, 0, 0, 0, 0,
0, 1, 2, 2, 0, 2, 0, 1, 0, 1, 0, 1, 0, 0, 2, 2, 2, 2, 2, 1, 2, 2,
2, 0, 0, 0, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 2, 2,
1, 1, 1, 1, 1, 1, 1])
In [411]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[411]:
array([1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0,
0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 2, 0, 2, 0, 2, 1, 1, 1, 0, 0, 1,
0, 0, 0, 0, 1, 2, 2, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 2, 2, 2, 0,
1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2,
2, 1, 0, 1, 0, 2, 2, 2, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,
1, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 1, 1, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 2, 2, 2, 1, 1, 0, 0, 1,
1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 2, 0, 0, 0,
0, 0, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 0, 0, 0,
1, 1, 2, 2, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 0, 1, 1, 0, 0,
1, 1, 1, 0, 1, 1, 2, 2, 0, 1, 1, 0, 1, 1, 1, 0, 1, 2, 0, 0, 0, 0,
0, 1, 2, 2, 0, 2, 0, 1, 0, 1, 0, 1, 0, 0, 2, 2, 2, 2, 2, 1, 2, 2,
2, 0, 0, 0, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 2, 2,
1, 1, 1, 1, 1, 1, 1])
In [412]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [413]:
X
Out[413]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.713160 | 0.038243 | 2.143431 | 0.466379 | 2.480688 | 1.533552 | -0.564788 | -0.236267 | -0.737672 | 0.470152 | -0.935468 | 0.074931 | 1 | 0 |
| 1 | 1.315247 | 0.387677 | 1.394548 | -0.542923 | -0.194326 | 0.100084 | 0.191568 | 1.691930 | -0.164059 | 0.599296 | -0.175780 | 0.267042 | 1 | 0 |
| 2 | 1.737505 | -1.440944 | -1.114255 | 0.326790 | -1.157732 | -1.261521 | -1.130503 | 1.585017 | -1.367743 | -1.287569 | -0.805298 | -1.676436 | 0 | 0 |
| 3 | 1.737506 | -1.127088 | -0.672464 | 0.505231 | -0.830959 | -1.046716 | -0.912922 | 1.082754 | -0.919242 | -0.761758 | -1.023346 | -1.220716 | 0 | 0 |
| 4 | 1.737506 | 1.455410 | 0.333584 | 1.122628 | 1.139134 | -0.481280 | 0.129896 | 0.915597 | 0.333032 | -0.289806 | -0.236639 | -0.281891 | 1 | 0 |
| 5 | -1.313694 | -1.631596 | -0.954634 | 2.545293 | -0.841431 | -0.880402 | 1.098767 | 0.874224 | -1.935272 | -1.777115 | -1.259928 | -1.270550 | 0 | 0 |
| 6 | 0.343081 | -0.554660 | -0.305290 | 0.059946 | -0.331585 | 0.008117 | -0.092598 | 0.086112 | 1.237321 | -0.237146 | 0.239978 | 1.617512 | 1 | 0 |
| 7 | 1.109261 | -0.479083 | -0.201140 | 1.561772 | -0.314776 | 2.324993 | -0.123793 | 1.681972 | 0.723606 | 1.281028 | 1.332118 | 1.616723 | 1 | 0 |
| 8 | 1.737506 | 0.431527 | -0.310582 | -0.122085 | -0.401366 | 1.109393 | -0.731551 | -0.284884 | -0.401724 | 0.343534 | 0.512560 | 2.039796 | 1 | 0 |
| 9 | -1.315956 | -1.270342 | -0.635019 | -0.965063 | -0.677128 | -0.999133 | -0.673936 | 0.377158 | -0.740151 | 0.479661 | 1.359663 | -0.539737 | 0 | 0 |
| 10 | 1.592092 | 0.167770 | 1.676372 | 0.444937 | 1.198262 | 1.558372 | 0.695811 | 0.857869 | 0.417106 | 1.008119 | 1.359663 | 1.013913 | 1 | 0 |
| 11 | 1.737506 | 0.061168 | -0.198190 | -0.277339 | -0.315128 | -0.260607 | -0.037409 | 0.713144 | -0.442008 | -0.420659 | -0.067628 | 1.042894 | 1 | 0 |
| 12 | 1.047772 | 0.511390 | 0.649199 | -0.733636 | -0.679537 | -1.048667 | -0.618650 | -1.049439 | 1.237321 | 0.935768 | 0.609736 | 1.089900 | 1 | 0 |
| 13 | 0.985207 | 0.757573 | 0.728808 | 0.148076 | -0.402837 | -0.657586 | -0.341424 | 0.352814 | 1.237320 | 0.537897 | -0.314771 | 1.274813 | 1 | 0 |
| 14 | -1.092408 | 0.724395 | -0.854683 | -1.339781 | -1.038651 | -0.146198 | -1.046078 | -0.968884 | -0.640529 | -0.662849 | 1.359664 | -0.842083 | 0 | 0 |
| 15 | -0.257098 | -0.953113 | -0.826836 | -1.058296 | -1.143706 | -1.295604 | -0.932057 | 1.298111 | 0.062365 | 0.078659 | 1.359663 | -0.414103 | 0 | 0 |
| 16 | -1.038548 | -1.285038 | -0.897336 | -0.925038 | -1.321623 | -0.675496 | -1.174686 | -0.220940 | -1.236805 | -0.992367 | 1.359664 | -0.452244 | 0 | 0 |
| 17 | -0.743373 | -1.006359 | -0.304526 | -0.673197 | 2.468788 | 1.394753 | 2.061863 | -1.147252 | -0.314785 | -0.803420 | -0.543555 | 2.127294 | 2 | 0 |
| 18 | -0.506076 | 0.745202 | 0.557874 | -0.551711 | 2.482435 | 1.618435 | 3.352869 | -0.896426 | 0.632864 | -0.504095 | -0.396771 | 1.961034 | 2 | 0 |
| 19 | -0.619836 | -0.900602 | 0.192155 | -0.280406 | 1.602038 | 1.570403 | 3.352869 | -1.028553 | -0.325763 | -0.694475 | -0.448468 | 2.112782 | 2 | 0 |
| 20 | 0.236496 | 1.024573 | 2.143431 | 2.184352 | -1.309784 | 1.967834 | -1.174015 | -1.738864 | -1.705683 | -1.658302 | 1.034240 | -1.039738 | 0 | 0 |
| 21 | -0.969823 | 0.186258 | -0.649182 | -0.822502 | -1.173086 | -0.203826 | -1.106955 | -1.095550 | 0.611256 | -1.183571 | 1.359663 | -0.954603 | 0 | 0 |
| 22 | -0.477230 | -0.147047 | -0.472900 | -0.603956 | -1.185349 | 0.835564 | -0.993918 | -1.280801 | 1.237321 | -0.492384 | 1.208362 | -0.579080 | 0 | 0 |
| 23 | -0.402673 | -1.613310 | 0.859201 | -1.462753 | -1.304385 | 1.048578 | -1.175049 | -1.320789 | -2.133693 | 0.809224 | 1.359663 | -1.698122 | 0 | 0 |
| 24 | 1.036252 | -0.180424 | 2.143430 | -0.530143 | -0.581394 | 0.192607 | 0.189292 | 1.455548 | 0.372539 | 1.118521 | -0.352066 | 0.295735 | 1 | 0 |
| 25 | 1.009571 | -0.114262 | 0.491121 | -0.494523 | -0.560272 | -0.489051 | 0.310555 | -0.762806 | 0.638941 | 1.281027 | -0.377793 | 0.160975 | 1 | 0 |
| 26 | 1.737505 | -0.691965 | 0.794800 | -0.996395 | -0.859283 | 0.108625 | -0.179964 | -0.404455 | -0.407132 | 0.559305 | 0.490880 | -0.690673 | 0 | 0 |
| 27 | 0.952878 | -0.600113 | 1.564652 | -0.647102 | -0.354733 | 0.741931 | -0.850897 | -0.165104 | -1.270161 | 1.281028 | 0.689314 | -0.739641 | 1 | 0 |
| 28 | 0.090687 | -1.135658 | 1.025879 | 0.173394 | 0.141333 | 0.938735 | -0.460724 | 1.033120 | -0.675812 | 1.016227 | 1.359663 | -0.918936 | 1 | 0 |
| 29 | 1.334056 | 1.036213 | 0.310885 | 1.388872 | 0.536826 | 1.066414 | 2.035180 | 1.368130 | 1.215360 | 1.281028 | 0.571847 | 1.089988 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 285 | -0.818809 | 0.795687 | -0.418744 | -0.981876 | 0.290435 | -1.082166 | 0.916788 | -1.121030 | -0.872233 | -0.467637 | -0.673139 | 2.127294 | 2 | 1 |
| 286 | -1.350808 | -0.132215 | 1.939439 | -0.255670 | 1.080433 | -0.906077 | -0.076483 | 1.691930 | -1.760598 | -0.831051 | -1.664177 | 0.190914 | 2 | 1 |
| 287 | -0.268283 | -1.246560 | -0.948278 | -1.092787 | -0.912773 | -1.286685 | -1.170304 | -1.398130 | -0.955533 | 1.281028 | -1.249300 | -1.331567 | 0 | 1 |
| 288 | -1.499565 | -1.477911 | -1.400613 | -1.454657 | -0.189787 | -1.235822 | -1.124408 | -1.277436 | -1.382225 | 1.281028 | -1.456827 | -1.039202 | 0 | 1 |
| 289 | -0.316037 | -1.101863 | -0.979251 | -0.947212 | -0.382959 | -1.211602 | -1.164073 | -0.572011 | -0.586600 | 1.281028 | -1.035303 | -1.071429 | 0 | 1 |
| 290 | -1.429079 | -0.014357 | -0.502745 | -0.434442 | 2.818635 | 1.078430 | 2.198959 | -1.053548 | -2.088193 | -1.732987 | -1.761028 | -0.159791 | 2 | 1 |
| 291 | 0.142459 | 0.454640 | -1.378454 | 1.070823 | -1.223040 | 2.764417 | 1.542144 | -1.706716 | 0.232404 | -1.955043 | 0.868232 | -1.674045 | 0 | 1 |
| 292 | 0.369579 | 0.015402 | -0.629733 | 2.545293 | -0.222337 | 1.917223 | 0.555192 | -1.194461 | 0.161682 | -1.235773 | 0.462188 | -0.512327 | 1 | 1 |
| 293 | 1.737506 | -0.048004 | 1.588206 | -0.467110 | 0.510737 | 0.554321 | 0.532244 | 1.152385 | -0.138238 | 0.923415 | -0.494020 | 0.305075 | 1 | 1 |
| 294 | -0.072827 | -0.808718 | 2.143431 | -1.069392 | 0.333942 | 1.075808 | -0.574391 | 0.052452 | -1.091144 | 1.209060 | -1.203635 | -0.631894 | 1 | 1 |
| 295 | 1.096294 | -0.192606 | 0.838680 | -0.968919 | 0.814071 | 1.951969 | -0.304336 | -0.213012 | -0.860743 | 1.281028 | -0.964629 | -0.039173 | 1 | 1 |
| 296 | 1.197861 | -0.481339 | -0.039784 | 0.576523 | 0.719339 | 1.230426 | -0.013807 | 0.576144 | 1.237321 | 0.827248 | 0.863710 | 1.347974 | 1 | 1 |
| 297 | 0.831369 | -0.845061 | -0.412444 | -0.202774 | 0.499287 | 0.798141 | 1.143779 | 0.135898 | 0.903542 | 1.103870 | 1.359663 | 1.309758 | 1 | 1 |
| 298 | 1.129174 | -0.778390 | -0.347478 | 0.028754 | 0.896022 | 1.021079 | 1.609960 | 0.261949 | 0.607068 | 1.111758 | 1.359663 | 1.623317 | 1 | 1 |
| 299 | 1.235661 | 0.246339 | -0.055182 | -0.274201 | -0.453087 | -0.448112 | -0.693517 | -0.103535 | 1.237321 | 0.849533 | 0.778756 | 0.601407 | 1 | 1 |
| 300 | 1.617641 | 0.157580 | 0.320652 | -0.272952 | 0.399767 | 0.391633 | -0.493105 | 0.638211 | 0.910413 | 1.281028 | 0.611215 | 0.781573 | 1 | 1 |
| 301 | 0.904215 | -0.238528 | 0.650660 | -0.588166 | 0.058915 | -0.260010 | -0.734161 | 0.076079 | 0.334341 | 1.281028 | -0.009255 | 0.453380 | 1 | 1 |
| 302 | -1.571329 | 2.288385 | -0.858389 | 1.605747 | 0.877803 | -1.392311 | -0.299009 | -1.732948 | 0.377403 | -1.348885 | -1.797949 | 0.161259 | 2 | 1 |
| 303 | -1.111497 | 1.012712 | -0.373804 | -0.488606 | -0.534590 | -1.065890 | -0.687799 | -1.079827 | 1.237321 | -0.872941 | -1.357570 | -1.172663 | 0 | 1 |
| 304 | -0.632774 | 2.288385 | 1.193051 | 0.583811 | 1.042355 | -0.849096 | 1.848927 | -1.561165 | 0.223166 | -0.146801 | -1.172130 | -0.154950 | 2 | 1 |
| 305 | -1.675063 | 0.049104 | -0.121444 | -1.322550 | -0.298216 | -1.383538 | 3.352869 | -0.922058 | -2.012971 | -0.306843 | -1.798988 | 0.307357 | 2 | 1 |
| 306 | -1.675063 | -0.302934 | 1.841519 | -1.463728 | 0.044986 | -1.392689 | 2.251379 | 0.179143 | -2.131740 | 1.281028 | -1.476204 | 1.119567 | 2 | 1 |
| 307 | -1.675063 | -0.345332 | 0.759301 | -1.463728 | -0.944610 | -1.392689 | 2.839571 | -1.657298 | -2.134915 | 1.281027 | -1.798988 | -0.818327 | 2 | 1 |
| 308 | -0.701240 | -0.718707 | 2.143431 | -0.317960 | 0.753769 | 0.883849 | 0.080036 | 0.067906 | -1.106120 | 0.663348 | -0.295994 | -0.620494 | 1 | 1 |
| 309 | 0.283947 | -0.770573 | 2.143431 | -0.677678 | 0.254128 | 1.306505 | -0.471240 | 1.183162 | -1.285117 | 0.322375 | 0.125126 | -0.725452 | 1 | 1 |
| 310 | -0.684797 | -0.424879 | 1.965265 | 0.285506 | 1.654633 | 1.714850 | 0.379435 | 0.747071 | -0.436120 | 1.281028 | 0.814894 | -0.455133 | 1 | 1 |
| 311 | -0.063614 | 0.387844 | 1.083088 | -0.000037 | 0.092909 | 0.624221 | 0.267615 | 1.377100 | 1.237321 | 0.720428 | 0.122085 | 0.422431 | 1 | 1 |
| 312 | 1.272215 | 0.276107 | 2.143431 | -0.015484 | 1.545799 | 1.751362 | 0.646849 | 1.047746 | 0.159537 | 0.294111 | -0.397554 | 0.554605 | 1 | 1 |
| 313 | -1.349102 | 0.408011 | -0.212504 | 2.027495 | 1.056566 | -0.074589 | 0.348765 | 1.691929 | 1.002332 | 0.337931 | 0.708780 | -0.469953 | 1 | 1 |
| 314 | 0.377466 | -0.342265 | -0.366249 | -0.135576 | -0.759547 | 1.018877 | -0.961553 | 1.691929 | 0.534889 | 0.262599 | -0.220736 | 0.127432 | 1 | 1 |
315 rows × 14 columns
In [414]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[414]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b82f796be0>
In [415]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[1]))
Club De Banqueros y Empresarios¶
ANN¶
In [274]:
X = df_n_ps_std_ch[1]
In [275]:
y = df_n_ps[1]['chosen']
In [276]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [277]:
X_train.shape
Out[277]:
(191, 12)
In [278]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [279]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [280]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [423]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (10, 10), 'learning_rate_init': 0.003, 'max_iter': 200}, que permiten obtener un Accuracy de 76.96% y un Kappa del 34.08
Tiempo total: 25.99 minutos
In [281]:
grid.best_params_ = {'activation': 'tanh', 'hidden_layer_sizes': (10, 10), 'learning_rate_init': 0.003, 'max_iter': 200}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [282]:
input_tensor = Input(shape = (n0,))
In [283]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [284]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [285]:
model.summary()
Model: "model_16" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_17 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_46 (Dense) (None, 10) 130 _________________________________________________________________ dense_47 (Dense) (None, 10) 110 _________________________________________________________________ dense_48 (Dense) (None, 1) 11 ================================================================= Total params: 251 Trainable params: 251 Non-trainable params: 0 _________________________________________________________________
In [286]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 191 samples, validate on 64 samples Epoch 1/200 191/191 [==============================] - 0s 1ms/step - loss: 0.7087 - accuracy: 0.5288 - val_loss: 0.7177 - val_accuracy: 0.5000 Epoch 2/200 191/191 [==============================] - 0s 84us/step - loss: 0.6727 - accuracy: 0.5916 - val_loss: 0.6935 - val_accuracy: 0.5312 Epoch 3/200 191/191 [==============================] - 0s 84us/step - loss: 0.6477 - accuracy: 0.6440 - val_loss: 0.6732 - val_accuracy: 0.5469 Epoch 4/200 191/191 [==============================] - 0s 89us/step - loss: 0.6268 - accuracy: 0.6859 - val_loss: 0.6566 - val_accuracy: 0.5781 Epoch 5/200 191/191 [==============================] - 0s 89us/step - loss: 0.6068 - accuracy: 0.7068 - val_loss: 0.6425 - val_accuracy: 0.6094 Epoch 6/200 191/191 [==============================] - 0s 89us/step - loss: 0.5911 - accuracy: 0.7173 - val_loss: 0.6319 - val_accuracy: 0.6094 Epoch 7/200 191/191 [==============================] - 0s 89us/step - loss: 0.5783 - accuracy: 0.7225 - val_loss: 0.6250 - val_accuracy: 0.6250 Epoch 8/200 191/191 [==============================] - 0s 84us/step - loss: 0.5665 - accuracy: 0.7277 - val_loss: 0.6181 - val_accuracy: 0.6406 Epoch 9/200 191/191 [==============================] - 0s 78us/step - loss: 0.5562 - accuracy: 0.7277 - val_loss: 0.6133 - val_accuracy: 0.6250 Epoch 10/200 191/191 [==============================] - 0s 84us/step - loss: 0.5488 - accuracy: 0.7435 - val_loss: 0.6092 - val_accuracy: 0.6406 Epoch 11/200 191/191 [==============================] - 0s 94us/step - loss: 0.5408 - accuracy: 0.7487 - val_loss: 0.6059 - val_accuracy: 0.6406 Epoch 12/200 191/191 [==============================] - 0s 89us/step - loss: 0.5354 - accuracy: 0.7435 - val_loss: 0.6040 - val_accuracy: 0.6406 Epoch 13/200 191/191 [==============================] - 0s 94us/step - loss: 0.5295 - accuracy: 0.7435 - val_loss: 0.6033 - val_accuracy: 0.6406 Epoch 14/200 191/191 [==============================] - 0s 99us/step - loss: 0.5244 - accuracy: 0.7487 - val_loss: 0.6023 - val_accuracy: 0.6406 Epoch 15/200 191/191 [==============================] - 0s 89us/step - loss: 0.5197 - accuracy: 0.7487 - val_loss: 0.6011 - val_accuracy: 0.6406 Epoch 16/200 191/191 [==============================] - 0s 94us/step - loss: 0.5159 - accuracy: 0.7487 - val_loss: 0.6002 - val_accuracy: 0.6406 Epoch 17/200 191/191 [==============================] - 0s 84us/step - loss: 0.5119 - accuracy: 0.7487 - val_loss: 0.6004 - val_accuracy: 0.6562 Epoch 18/200 191/191 [==============================] - 0s 84us/step - loss: 0.5089 - accuracy: 0.7539 - val_loss: 0.6029 - val_accuracy: 0.6562 Epoch 19/200 191/191 [==============================] - 0s 89us/step - loss: 0.5054 - accuracy: 0.7487 - val_loss: 0.6033 - val_accuracy: 0.6562 Epoch 20/200 191/191 [==============================] - 0s 94us/step - loss: 0.5018 - accuracy: 0.7487 - val_loss: 0.6033 - val_accuracy: 0.6562 Epoch 21/200 191/191 [==============================] - 0s 89us/step - loss: 0.4993 - accuracy: 0.7487 - val_loss: 0.6037 - val_accuracy: 0.6562 Epoch 22/200 191/191 [==============================] - 0s 84us/step - loss: 0.4957 - accuracy: 0.7487 - val_loss: 0.6056 - val_accuracy: 0.6562 Epoch 23/200 191/191 [==============================] - 0s 105us/step - loss: 0.4934 - accuracy: 0.7539 - val_loss: 0.6100 - val_accuracy: 0.6719 Epoch 24/200 191/191 [==============================] - 0s 99us/step - loss: 0.4887 - accuracy: 0.7487 - val_loss: 0.6113 - val_accuracy: 0.6719 Epoch 25/200 191/191 [==============================] - 0s 89us/step - loss: 0.4866 - accuracy: 0.7435 - val_loss: 0.6083 - val_accuracy: 0.6719 Epoch 26/200 191/191 [==============================] - 0s 84us/step - loss: 0.4831 - accuracy: 0.7435 - val_loss: 0.6124 - val_accuracy: 0.6719 Epoch 27/200 191/191 [==============================] - 0s 89us/step - loss: 0.4802 - accuracy: 0.7487 - val_loss: 0.6134 - val_accuracy: 0.6719 Epoch 28/200 191/191 [==============================] - 0s 89us/step - loss: 0.4769 - accuracy: 0.7487 - val_loss: 0.6106 - val_accuracy: 0.6719 Epoch 29/200 191/191 [==============================] - 0s 84us/step - loss: 0.4738 - accuracy: 0.7539 - val_loss: 0.6102 - val_accuracy: 0.6719 Epoch 30/200 191/191 [==============================] - 0s 89us/step - loss: 0.4712 - accuracy: 0.7539 - val_loss: 0.6098 - val_accuracy: 0.6719 Epoch 31/200 191/191 [==============================] - 0s 89us/step - loss: 0.4673 - accuracy: 0.7487 - val_loss: 0.6118 - val_accuracy: 0.6875 Epoch 32/200 191/191 [==============================] - 0s 89us/step - loss: 0.4650 - accuracy: 0.7487 - val_loss: 0.6131 - val_accuracy: 0.6875 Epoch 33/200 191/191 [==============================] - 0s 99us/step - loss: 0.4620 - accuracy: 0.7592 - val_loss: 0.6143 - val_accuracy: 0.6875 Epoch 34/200 191/191 [==============================] - 0s 84us/step - loss: 0.4588 - accuracy: 0.7592 - val_loss: 0.6144 - val_accuracy: 0.6875 Epoch 35/200 191/191 [==============================] - 0s 94us/step - loss: 0.4564 - accuracy: 0.7592 - val_loss: 0.6145 - val_accuracy: 0.6875 Epoch 36/200 191/191 [==============================] - 0s 89us/step - loss: 0.4530 - accuracy: 0.7644 - val_loss: 0.6145 - val_accuracy: 0.6875 Epoch 37/200 191/191 [==============================] - 0s 94us/step - loss: 0.4513 - accuracy: 0.7592 - val_loss: 0.6170 - val_accuracy: 0.6875 Epoch 38/200 191/191 [==============================] - 0s 105us/step - loss: 0.4475 - accuracy: 0.7592 - val_loss: 0.6191 - val_accuracy: 0.6875 Epoch 39/200 191/191 [==============================] - 0s 120us/step - loss: 0.4449 - accuracy: 0.7644 - val_loss: 0.6166 - val_accuracy: 0.6875 Epoch 40/200 191/191 [==============================] - 0s 94us/step - loss: 0.4427 - accuracy: 0.7696 - val_loss: 0.6174 - val_accuracy: 0.6875 Epoch 41/200 191/191 [==============================] - 0s 89us/step - loss: 0.4406 - accuracy: 0.7749 - val_loss: 0.6191 - val_accuracy: 0.6875 Epoch 00041: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 42/200 191/191 [==============================] - 0s 89us/step - loss: 0.4361 - accuracy: 0.7749 - val_loss: 0.6193 - val_accuracy: 0.6875 Epoch 43/200 191/191 [==============================] - 0s 89us/step - loss: 0.4351 - accuracy: 0.7749 - val_loss: 0.6197 - val_accuracy: 0.6875 Epoch 44/200 191/191 [==============================] - 0s 105us/step - loss: 0.4332 - accuracy: 0.7749 - val_loss: 0.6205 - val_accuracy: 0.6875 Epoch 45/200 191/191 [==============================] - 0s 94us/step - loss: 0.4318 - accuracy: 0.7749 - val_loss: 0.6214 - val_accuracy: 0.6875 Epoch 46/200 191/191 [==============================] - 0s 94us/step - loss: 0.4304 - accuracy: 0.7749 - val_loss: 0.6216 - val_accuracy: 0.6875 Epoch 47/200 191/191 [==============================] - 0s 99us/step - loss: 0.4291 - accuracy: 0.7696 - val_loss: 0.6220 - val_accuracy: 0.6875 Epoch 48/200 191/191 [==============================] - 0s 94us/step - loss: 0.4274 - accuracy: 0.7696 - val_loss: 0.6221 - val_accuracy: 0.6875 Epoch 49/200 191/191 [==============================] - 0s 99us/step - loss: 0.4264 - accuracy: 0.7749 - val_loss: 0.6231 - val_accuracy: 0.6875 Epoch 50/200 191/191 [==============================] - 0s 99us/step - loss: 0.4249 - accuracy: 0.7801 - val_loss: 0.6225 - val_accuracy: 0.6875 Epoch 51/200 191/191 [==============================] - 0s 94us/step - loss: 0.4231 - accuracy: 0.7801 - val_loss: 0.6233 - val_accuracy: 0.6875 Epoch 00051: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 52/200 191/191 [==============================] - 0s 94us/step - loss: 0.4219 - accuracy: 0.7801 - val_loss: 0.6237 - val_accuracy: 0.6875 Epoch 53/200 191/191 [==============================] - 0s 89us/step - loss: 0.4209 - accuracy: 0.7801 - val_loss: 0.6246 - val_accuracy: 0.6875 Epoch 54/200 191/191 [==============================] - 0s 94us/step - loss: 0.4205 - accuracy: 0.7853 - val_loss: 0.6255 - val_accuracy: 0.6875 Epoch 55/200 191/191 [==============================] - 0s 94us/step - loss: 0.4197 - accuracy: 0.7853 - val_loss: 0.6260 - val_accuracy: 0.6875 Epoch 56/200 191/191 [==============================] - 0s 89us/step - loss: 0.4189 - accuracy: 0.7853 - val_loss: 0.6265 - val_accuracy: 0.6875 Epoch 57/200 191/191 [==============================] - 0s 89us/step - loss: 0.4183 - accuracy: 0.7906 - val_loss: 0.6265 - val_accuracy: 0.6875 Epoch 58/200 191/191 [==============================] - 0s 94us/step - loss: 0.4177 - accuracy: 0.7906 - val_loss: 0.6259 - val_accuracy: 0.6875 Epoch 59/200 191/191 [==============================] - 0s 84us/step - loss: 0.4166 - accuracy: 0.7906 - val_loss: 0.6263 - val_accuracy: 0.6875 Epoch 60/200 191/191 [==============================] - 0s 84us/step - loss: 0.4163 - accuracy: 0.7906 - val_loss: 0.6273 - val_accuracy: 0.6875 Epoch 61/200 191/191 [==============================] - 0s 105us/step - loss: 0.4153 - accuracy: 0.7853 - val_loss: 0.6279 - val_accuracy: 0.6875 Epoch 00061: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 62/200 191/191 [==============================] - 0s 94us/step - loss: 0.4146 - accuracy: 0.7906 - val_loss: 0.6280 - val_accuracy: 0.6875 Epoch 63/200 191/191 [==============================] - 0s 89us/step - loss: 0.4142 - accuracy: 0.7906 - val_loss: 0.6279 - val_accuracy: 0.6875 Epoch 64/200 191/191 [==============================] - 0s 84us/step - loss: 0.4139 - accuracy: 0.7906 - val_loss: 0.6278 - val_accuracy: 0.6875 Epoch 65/200 191/191 [==============================] - 0s 84us/step - loss: 0.4136 - accuracy: 0.7906 - val_loss: 0.6280 - val_accuracy: 0.6875 Epoch 66/200 191/191 [==============================] - 0s 89us/step - loss: 0.4131 - accuracy: 0.7906 - val_loss: 0.6283 - val_accuracy: 0.6875 Epoch 67/200 191/191 [==============================] - 0s 94us/step - loss: 0.4128 - accuracy: 0.7906 - val_loss: 0.6285 - val_accuracy: 0.6875 Epoch 68/200 191/191 [==============================] - 0s 84us/step - loss: 0.4125 - accuracy: 0.7906 - val_loss: 0.6286 - val_accuracy: 0.6875 Epoch 69/200 191/191 [==============================] - 0s 89us/step - loss: 0.4120 - accuracy: 0.7906 - val_loss: 0.6286 - val_accuracy: 0.6875 Epoch 70/200 191/191 [==============================] - 0s 84us/step - loss: 0.4117 - accuracy: 0.7906 - val_loss: 0.6288 - val_accuracy: 0.6875 Epoch 71/200 191/191 [==============================] - 0s 99us/step - loss: 0.4113 - accuracy: 0.7906 - val_loss: 0.6289 - val_accuracy: 0.6875 Epoch 00071: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 72/200 191/191 [==============================] - 0s 126us/step - loss: 0.4110 - accuracy: 0.7906 - val_loss: 0.6290 - val_accuracy: 0.6875 Epoch 73/200 191/191 [==============================] - 0s 110us/step - loss: 0.4108 - accuracy: 0.7906 - val_loss: 0.6289 - val_accuracy: 0.6875 Epoch 74/200 191/191 [==============================] - 0s 105us/step - loss: 0.4107 - accuracy: 0.7906 - val_loss: 0.6291 - val_accuracy: 0.6875 Epoch 75/200 191/191 [==============================] - 0s 94us/step - loss: 0.4105 - accuracy: 0.7906 - val_loss: 0.6291 - val_accuracy: 0.6875 Epoch 76/200 191/191 [==============================] - 0s 94us/step - loss: 0.4103 - accuracy: 0.7906 - val_loss: 0.6291 - val_accuracy: 0.6875 Epoch 77/200 191/191 [==============================] - 0s 89us/step - loss: 0.4101 - accuracy: 0.7906 - val_loss: 0.6294 - val_accuracy: 0.6875 Epoch 78/200 191/191 [==============================] - 0s 89us/step - loss: 0.4100 - accuracy: 0.7906 - val_loss: 0.6295 - val_accuracy: 0.6875 Epoch 79/200 191/191 [==============================] - 0s 84us/step - loss: 0.4098 - accuracy: 0.7906 - val_loss: 0.6295 - val_accuracy: 0.6875 Epoch 80/200 191/191 [==============================] - 0s 99us/step - loss: 0.4096 - accuracy: 0.7906 - val_loss: 0.6297 - val_accuracy: 0.6875 Epoch 81/200 191/191 [==============================] - 0s 89us/step - loss: 0.4095 - accuracy: 0.7906 - val_loss: 0.6297 - val_accuracy: 0.6875 Epoch 00081: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 82/200 191/191 [==============================] - 0s 84us/step - loss: 0.4093 - accuracy: 0.7906 - val_loss: 0.6297 - val_accuracy: 0.6875 Epoch 83/200 191/191 [==============================] - 0s 94us/step - loss: 0.4092 - accuracy: 0.7906 - val_loss: 0.6298 - val_accuracy: 0.6875 Epoch 84/200 191/191 [==============================] - 0s 94us/step - loss: 0.4091 - accuracy: 0.7906 - val_loss: 0.6298 - val_accuracy: 0.6875 Epoch 85/200 191/191 [==============================] - 0s 89us/step - loss: 0.4090 - accuracy: 0.7906 - val_loss: 0.6298 - val_accuracy: 0.6875 Epoch 86/200 191/191 [==============================] - 0s 84us/step - loss: 0.4089 - accuracy: 0.7906 - val_loss: 0.6299 - val_accuracy: 0.6875 Epoch 87/200 191/191 [==============================] - 0s 94us/step - loss: 0.4088 - accuracy: 0.7906 - val_loss: 0.6300 - val_accuracy: 0.6875 Epoch 88/200 191/191 [==============================] - 0s 89us/step - loss: 0.4087 - accuracy: 0.7906 - val_loss: 0.6299 - val_accuracy: 0.6875 Epoch 89/200 191/191 [==============================] - 0s 84us/step - loss: 0.4087 - accuracy: 0.7906 - val_loss: 0.6300 - val_accuracy: 0.6875 Epoch 90/200 191/191 [==============================] - 0s 89us/step - loss: 0.4086 - accuracy: 0.7906 - val_loss: 0.6301 - val_accuracy: 0.6875 Epoch 91/200 191/191 [==============================] - 0s 94us/step - loss: 0.4085 - accuracy: 0.7906 - val_loss: 0.6302 - val_accuracy: 0.6875 Epoch 00091: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 92/200 191/191 [==============================] - 0s 94us/step - loss: 0.4084 - accuracy: 0.7906 - val_loss: 0.6302 - val_accuracy: 0.6875 Epoch 93/200 191/191 [==============================] - 0s 89us/step - loss: 0.4083 - accuracy: 0.7906 - val_loss: 0.6302 - val_accuracy: 0.6875 Epoch 94/200 191/191 [==============================] - 0s 94us/step - loss: 0.4083 - accuracy: 0.7906 - val_loss: 0.6302 - val_accuracy: 0.6875 Epoch 95/200 191/191 [==============================] - 0s 99us/step - loss: 0.4082 - accuracy: 0.7906 - val_loss: 0.6302 - val_accuracy: 0.6875 Epoch 96/200 191/191 [==============================] - 0s 105us/step - loss: 0.4082 - accuracy: 0.7906 - val_loss: 0.6303 - val_accuracy: 0.6875 Epoch 97/200 191/191 [==============================] - 0s 105us/step - loss: 0.4082 - accuracy: 0.7906 - val_loss: 0.6303 - val_accuracy: 0.6875 Epoch 98/200 191/191 [==============================] - 0s 99us/step - loss: 0.4081 - accuracy: 0.7906 - val_loss: 0.6303 - val_accuracy: 0.6875 Epoch 99/200 191/191 [==============================] - 0s 99us/step - loss: 0.4081 - accuracy: 0.7906 - val_loss: 0.6303 - val_accuracy: 0.6875 Epoch 100/200 191/191 [==============================] - 0s 105us/step - loss: 0.4080 - accuracy: 0.7906 - val_loss: 0.6303 - val_accuracy: 0.6875 Epoch 101/200 191/191 [==============================] - 0s 84us/step - loss: 0.4080 - accuracy: 0.7906 - val_loss: 0.6303 - val_accuracy: 0.6875 Epoch 00101: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 102/200 191/191 [==============================] - 0s 89us/step - loss: 0.4080 - accuracy: 0.7906 - val_loss: 0.6303 - val_accuracy: 0.6875 Epoch 103/200 191/191 [==============================] - 0s 94us/step - loss: 0.4079 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 104/200 191/191 [==============================] - 0s 94us/step - loss: 0.4079 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 105/200 191/191 [==============================] - 0s 110us/step - loss: 0.4079 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 106/200 191/191 [==============================] - 0s 89us/step - loss: 0.4079 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 107/200 191/191 [==============================] - 0s 99us/step - loss: 0.4078 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 108/200 191/191 [==============================] - 0s 89us/step - loss: 0.4078 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 109/200 191/191 [==============================] - 0s 115us/step - loss: 0.4078 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 110/200 191/191 [==============================] - 0s 105us/step - loss: 0.4078 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 111/200 191/191 [==============================] - 0s 136us/step - loss: 0.4078 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 00111: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 112/200 191/191 [==============================] - 0s 99us/step - loss: 0.4077 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 113/200 191/191 [==============================] - 0s 94us/step - loss: 0.4077 - accuracy: 0.7906 - val_loss: 0.6304 - val_accuracy: 0.6875 Epoch 114/200 191/191 [==============================] - 0s 89us/step - loss: 0.4077 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 115/200 191/191 [==============================] - 0s 99us/step - loss: 0.4077 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 116/200 191/191 [==============================] - 0s 89us/step - loss: 0.4077 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 117/200 191/191 [==============================] - 0s 94us/step - loss: 0.4077 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 118/200 191/191 [==============================] - 0s 94us/step - loss: 0.4077 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 119/200 191/191 [==============================] - 0s 115us/step - loss: 0.4077 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 120/200 191/191 [==============================] - 0s 84us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 121/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 00121: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 122/200 191/191 [==============================] - 0s 99us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 123/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 124/200 191/191 [==============================] - 0s 99us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 125/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 126/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 127/200 191/191 [==============================] - 0s 99us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 128/200 191/191 [==============================] - 0s 94us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 129/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 130/200 191/191 [==============================] - 0s 94us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 131/200 191/191 [==============================] - 0s 94us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 00131: ReduceLROnPlateau reducing learning rate to 2.9296875254658516e-06. Epoch 132/200 191/191 [==============================] - 0s 105us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 133/200 191/191 [==============================] - 0s 94us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 134/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 135/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 136/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 137/200 191/191 [==============================] - 0s 94us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 138/200 191/191 [==============================] - 0s 84us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 139/200 191/191 [==============================] - 0s 84us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 140/200 191/191 [==============================] - 0s 84us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 141/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 00141: ReduceLROnPlateau reducing learning rate to 1.4648437627329258e-06. Epoch 142/200 191/191 [==============================] - 0s 84us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 143/200 191/191 [==============================] - 0s 89us/step - loss: 0.4076 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 144/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 145/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 146/200 191/191 [==============================] - 0s 84us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 147/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 148/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 149/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 150/200 191/191 [==============================] - 0s 84us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 151/200 191/191 [==============================] - 0s 120us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 00151: ReduceLROnPlateau reducing learning rate to 7.324218813664629e-07. Epoch 152/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 153/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 154/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 155/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 156/200 191/191 [==============================] - 0s 105us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 157/200 191/191 [==============================] - 0s 110us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 158/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 159/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 160/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 161/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 00161: ReduceLROnPlateau reducing learning rate to 3.6621094068323146e-07. Epoch 162/200 191/191 [==============================] - 0s 99us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 163/200 191/191 [==============================] - 0s 105us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 164/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6305 - val_accuracy: 0.6875 Epoch 165/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 166/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 167/200 191/191 [==============================] - 0s 99us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 168/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 169/200 191/191 [==============================] - 0s 99us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 170/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 171/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 00171: ReduceLROnPlateau reducing learning rate to 1.8310547034161573e-07. Epoch 172/200 191/191 [==============================] - 0s 99us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 173/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 174/200 191/191 [==============================] - 0s 84us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 175/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 176/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 177/200 191/191 [==============================] - 0s 115us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 178/200 191/191 [==============================] - 0s 99us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 179/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 180/200 191/191 [==============================] - 0s 110us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 181/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 00181: ReduceLROnPlateau reducing learning rate to 9.155273517080786e-08. Epoch 182/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 183/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 184/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 185/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 186/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 187/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 188/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 189/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 190/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 191/200 191/191 [==============================] - 0s 99us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 00191: ReduceLROnPlateau reducing learning rate to 4.577636758540393e-08. Epoch 192/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 193/200 191/191 [==============================] - 0s 84us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 194/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 195/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 196/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 197/200 191/191 [==============================] - 0s 99us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 198/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 199/200 191/191 [==============================] - 0s 94us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875 Epoch 200/200 191/191 [==============================] - 0s 89us/step - loss: 0.4075 - accuracy: 0.7906 - val_loss: 0.6306 - val_accuracy: 0.6875
In [287]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 200)
In [288]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
64/64 [==============================] - 0s 62us/step test loss: 0.6305528879165649, test accuracy: 0.6875
In [289]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6107634543178974
In [290]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.013867488443759624 [[42 5] [15 2]]
KMeans¶
In [434]:
X
Out[434]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.369691 | -0.881824 | -0.095656 | -0.923999 | -0.030645 | -0.834931 | -1.031650 | -0.840942 | -0.677716 | 1.084098 | -1.064999 | -1.156623 |
| 1 | -0.175875 | -0.403800 | -0.657709 | -0.201259 | 1.691433 | -0.672783 | -0.119944 | -0.440080 | 0.339906 | 1.084098 | 0.504608 | 0.931676 |
| 2 | 0.894452 | -0.189794 | 1.959063 | 0.169276 | -0.403611 | -1.036954 | 1.447615 | -0.340767 | -0.846170 | -0.515065 | -0.699878 | 0.032355 |
| 3 | 0.060782 | -0.392075 | 0.826233 | -0.048480 | 1.789786 | -0.552163 | 0.121028 | -0.111355 | 0.220614 | 1.084098 | 0.073241 | 1.176257 |
| 4 | -1.116536 | -0.923742 | -1.238971 | -0.919598 | 0.313068 | -1.160111 | 1.316032 | -0.700013 | -1.600210 | 1.084098 | -1.072155 | 1.270095 |
| 5 | -1.082752 | -0.067472 | -1.142511 | -0.923999 | 0.389201 | -1.061180 | 2.067035 | -0.269819 | -1.620482 | -0.468180 | -0.962315 | 1.853511 |
| 6 | -1.202528 | -0.776453 | -1.135820 | -0.773268 | -0.409932 | -0.832224 | -0.631931 | -0.119492 | 1.620288 | 0.162930 | -1.063358 | 0.001987 |
| 7 | -1.206944 | 0.500703 | -0.243295 | -0.611928 | 0.262760 | -0.855004 | 0.001441 | -1.579225 | 0.245997 | 1.084098 | -1.197067 | -0.681282 |
| 8 | -0.982006 | 0.421831 | 0.984997 | -0.550391 | 0.215104 | -1.100712 | 2.248321 | -0.336428 | 1.282109 | 1.084098 | -0.061910 | 0.480931 |
| 9 | 1.703175 | 1.029234 | 1.508859 | 0.964653 | 2.164165 | 0.482073 | -0.794175 | 0.729102 | 0.721867 | 0.439251 | 0.198086 | 1.778147 |
| 10 | 1.528139 | 0.898498 | 0.923889 | 0.634045 | 2.016059 | 0.674138 | -0.430188 | 0.558129 | 1.200855 | 0.618822 | 0.291110 | 1.853511 |
| 11 | 0.334361 | -0.301383 | -0.450307 | -0.470199 | -0.977542 | 0.863046 | -0.396657 | -0.882307 | 0.259614 | 0.809320 | 1.750633 | 0.245556 |
| 12 | 0.597458 | 0.773201 | 0.182265 | 0.104921 | 0.580017 | 0.644184 | 1.433111 | 1.735353 | -0.712181 | -0.818426 | -0.129551 | -0.039236 |
| 13 | -0.493625 | 1.341798 | -0.632970 | 2.666081 | 2.015672 | 1.537362 | 2.432631 | 0.500840 | 1.337627 | 1.084098 | -0.069418 | 1.097765 |
| 14 | 0.962230 | 0.028408 | 0.059003 | -0.233385 | 1.425585 | 1.226062 | 1.160066 | 1.604723 | 0.272753 | 1.084098 | 0.709073 | 1.356660 |
| 15 | -0.843247 | 0.160055 | 1.959063 | -0.354971 | -0.294051 | -0.485118 | -0.796417 | -0.212355 | -0.168152 | -0.782723 | -0.232169 | -0.234956 |
| 16 | -0.256403 | 1.322075 | 1.520118 | 0.907583 | 1.032003 | 0.577931 | 0.414295 | 0.551597 | 0.783033 | 1.084098 | 0.088744 | 1.848078 |
| 17 | -0.456352 | 2.142046 | 1.959063 | 1.734877 | 1.472675 | 0.985568 | 0.646614 | 0.230853 | 0.051480 | -1.122573 | -0.594948 | 0.587848 |
| 18 | -0.637040 | -1.030219 | -1.165495 | -0.606800 | 2.164165 | -1.161299 | -0.882343 | 1.727762 | -1.021803 | -1.420384 | -1.197067 | -1.157909 |
| 19 | -1.204564 | -1.022455 | -0.683305 | -0.923999 | -0.935167 | -0.988596 | -0.688535 | 1.735353 | -0.372860 | -1.581696 | -1.197067 | -0.315002 |
| 20 | -1.077420 | -0.314272 | 1.036699 | -0.850174 | -0.071202 | -1.104286 | 0.102157 | -0.867378 | -0.464391 | 0.591092 | -1.048902 | 1.853510 |
| 21 | -0.494728 | -1.023105 | -0.383945 | -0.918858 | 0.498899 | -1.042513 | -0.072226 | -0.187025 | -0.589871 | 1.084098 | -0.986967 | 0.537859 |
| 22 | -0.525116 | 0.187277 | 1.220635 | -0.221678 | 0.589822 | -1.115053 | 0.421737 | 0.656325 | -0.098846 | 1.084098 | -0.740109 | 1.853156 |
| 23 | -0.282675 | 0.571926 | -0.333097 | 2.860439 | 1.250860 | 1.434107 | 2.006573 | 0.362041 | 1.620288 | 0.896295 | -0.274184 | 1.257508 |
| 24 | 1.898264 | 1.240876 | 0.302806 | -0.427292 | -0.709276 | -0.159183 | -0.474972 | 0.273501 | -0.789908 | -0.085745 | 0.868592 | -0.492577 |
| 25 | 0.449834 | -0.315494 | -0.187659 | -0.426842 | -0.875213 | 0.916315 | -0.212134 | -0.503325 | 0.251438 | 0.499009 | 1.750633 | 0.286629 |
| 26 | -0.745212 | -0.457525 | -0.261214 | 1.653228 | 0.471865 | -0.532478 | 3.048431 | -0.344057 | 0.968569 | -1.690268 | 0.186111 | -0.576484 |
| 27 | -1.146960 | 2.737908 | 0.205319 | -0.915593 | 1.224042 | -1.080890 | 2.432039 | -1.475399 | 1.443033 | -0.136290 | -1.161316 | 1.353237 |
| 28 | -0.006670 | 2.177984 | -0.364684 | -0.232426 | 2.164165 | -0.501370 | 0.481222 | -0.462802 | -0.495909 | 0.200246 | -0.925963 | -0.127350 |
| 29 | -1.198149 | 1.245381 | 1.824748 | -0.544122 | 2.094805 | -0.986543 | 1.141795 | -1.276929 | 1.620288 | 0.772653 | -1.183303 | -0.633811 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | -0.850302 | -0.813520 | -1.238258 | 0.389340 | -0.820553 | -0.147948 | -0.867381 | -1.187728 | 1.620288 | -0.228859 | 0.676545 | -1.166259 |
| 226 | 1.113887 | -0.637363 | -0.178882 | -0.867049 | -0.180374 | -0.059762 | -1.031280 | 1.075930 | 1.620288 | -0.831212 | -0.536781 | -0.852284 |
| 227 | -0.939216 | -1.028651 | -1.083821 | 0.671288 | -1.104197 | 0.395233 | -0.935964 | -0.545426 | -0.734897 | -1.634988 | 1.750633 | -0.991450 |
| 228 | 0.323318 | -0.538560 | 1.959063 | 0.469737 | -0.928167 | 0.315432 | -0.838357 | 0.553622 | -0.985928 | -0.404420 | 0.017683 | -0.868443 |
| 229 | 0.651351 | 0.768776 | 1.651644 | 0.613579 | -0.672599 | 0.778338 | -0.288893 | 1.735353 | -0.698435 | -0.803492 | 0.749774 | -0.176628 |
| 230 | 1.208263 | 0.590562 | -0.856200 | -0.037362 | -0.890995 | 0.154056 | -0.224548 | 0.060386 | 1.620288 | -0.577590 | 1.081262 | 0.137052 |
| 231 | 1.629732 | -0.424079 | -0.793853 | -0.297522 | 0.876511 | -0.502033 | -0.540340 | 1.735353 | 1.179917 | 0.331082 | -0.186926 | 0.043014 |
| 232 | 1.097162 | 0.374225 | -0.897801 | -0.315126 | 0.016346 | 0.367805 | -0.397202 | 0.034201 | 0.736455 | 1.084098 | 1.111692 | 0.505493 |
| 233 | 1.452404 | -0.818664 | 1.001952 | 0.094142 | -1.157451 | -0.673136 | -1.031650 | -0.681166 | -1.578572 | -1.040029 | 1.750633 | -1.088779 |
| 234 | 0.687938 | 0.363684 | -0.077785 | 1.495170 | -0.946513 | 0.162091 | -0.875163 | -0.486609 | -0.657582 | -0.728212 | 1.750633 | 0.166071 |
| 235 | -0.511818 | -1.019067 | 1.937312 | -0.923999 | -1.199069 | -1.157422 | 1.424573 | -1.506879 | -0.941792 | 1.084098 | -0.598433 | 1.497327 |
| 236 | -0.649452 | 1.110585 | 0.023607 | -0.619494 | -0.351503 | -0.377758 | 0.774664 | -1.037327 | -0.008921 | 1.084098 | -0.300447 | -0.799951 |
| 237 | -0.675917 | 0.864345 | 1.959063 | -0.702632 | 0.767520 | -0.242236 | -0.320118 | -0.889868 | -0.543499 | 0.953653 | -0.295863 | 0.184530 |
| 238 | -0.820946 | 0.480728 | -0.348445 | 1.706293 | -0.634861 | -0.548325 | -0.658463 | -1.446347 | -0.595881 | -1.346009 | 1.750633 | -0.891882 |
| 239 | 0.335654 | -0.570366 | 0.440736 | 2.255028 | -0.777152 | -0.336048 | -0.104033 | 0.504513 | -0.304387 | -0.899407 | 1.750633 | 0.586226 |
| 240 | 0.772849 | -0.288034 | 0.998235 | 2.707124 | 0.694491 | 1.600236 | -0.599878 | 0.863164 | 1.620288 | -0.793363 | 1.076398 | 0.313476 |
| 241 | -0.725775 | -0.253169 | -1.058923 | -0.019515 | -0.367824 | -0.703472 | 0.163373 | 0.093846 | 1.620288 | 0.712877 | -0.410881 | 0.776774 |
| 242 | -0.883133 | 0.370675 | -1.102573 | 0.232760 | -1.047240 | -0.968298 | 0.151912 | -0.346068 | 1.620288 | -0.219994 | -0.255762 | 0.629036 |
| 243 | -0.504299 | -0.158035 | -0.129250 | 0.170764 | 0.127833 | -0.424841 | 0.960604 | 0.067663 | 1.620288 | 0.679838 | 0.005478 | 0.547596 |
| 244 | -0.829496 | 0.743464 | 1.959063 | 0.266679 | 0.653670 | -1.095468 | 1.900161 | 1.007911 | -0.442746 | 0.870766 | -0.246038 | 1.728101 |
| 245 | -1.203285 | 0.455171 | 0.496797 | -0.873183 | 0.629642 | -1.113864 | 1.271226 | 0.542153 | -0.636367 | 1.084098 | -1.140705 | 0.222521 |
| 246 | -1.122600 | -0.442839 | 1.824660 | -0.819762 | 1.050840 | -1.048446 | 1.937596 | 0.131208 | -0.474964 | 0.536040 | -0.800922 | 1.853511 |
| 247 | -1.206944 | -0.795159 | -1.159900 | -0.187089 | 0.233445 | 2.452569 | 0.183293 | 1.253589 | -1.394630 | 0.753640 | -1.083641 | -1.163597 |
| 248 | -0.501382 | -1.030219 | -0.612979 | -0.923999 | -0.608020 | -0.863086 | -0.124132 | -0.944272 | -1.081271 | 1.084098 | -1.170461 | -1.244399 |
| 249 | -0.704093 | -0.985145 | 1.199508 | -0.321552 | 2.164165 | -0.193249 | -0.295412 | -0.394034 | 0.032818 | -1.672473 | -0.724683 | -1.067831 |
| 250 | 0.831957 | -0.173367 | 1.636565 | 1.345345 | -0.989257 | 0.826135 | -0.824412 | -0.072225 | -0.255975 | -1.005500 | 1.750633 | -1.067130 |
| 251 | -0.895156 | -1.022380 | -0.410545 | 2.237273 | -1.199069 | 0.687783 | -1.006468 | -0.694581 | -1.311235 | -1.270219 | 1.750633 | -1.233006 |
| 252 | -0.546320 | -0.631883 | -0.800789 | -0.187107 | -1.179353 | 0.464606 | -0.905192 | -0.113592 | 0.167123 | -0.843254 | 1.750633 | -1.148945 |
| 253 | -0.591349 | -0.947758 | -0.915262 | -0.579179 | 0.089961 | 0.047756 | -0.905554 | -1.413215 | -1.184716 | 1.084098 | 0.384684 | -1.035788 |
| 254 | -0.072947 | -0.912155 | 0.150662 | -0.246506 | -1.198328 | -0.779047 | -1.031650 | 0.022522 | -1.604883 | -1.609572 | 1.750632 | -1.247525 |
255 rows × 12 columns
In [435]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[435]:
[3060.0, 2594.1549165385713, 2309.549481414484, 2089.610498278143, 1959.0566777030967, 1842.9588281368096, 1736.092921360928, 1663.5689730025233, 1609.4951000525748, 1557.5767570007226, 1514.8225721032359, 1447.3960995377222, 1422.027087832329, 1389.3032827223215]
In [436]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[436]:
[<matplotlib.lines.Line2D at 0x1b82d902048>]
K=2
In [437]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[437]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [438]:
kmeans_ch.labels_
Out[438]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,
1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1])
In [439]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[439]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,
1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1])
In [440]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [441]:
X
Out[441]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.369691 | -0.881824 | -0.095656 | -0.923999 | -0.030645 | -0.834931 | -1.031650 | -0.840942 | -0.677716 | 1.084098 | -1.064999 | -1.156623 | 0 | 0 |
| 1 | -0.175875 | -0.403800 | -0.657709 | -0.201259 | 1.691433 | -0.672783 | -0.119944 | -0.440080 | 0.339906 | 1.084098 | 0.504608 | 0.931676 | 0 | 0 |
| 2 | 0.894452 | -0.189794 | 1.959063 | 0.169276 | -0.403611 | -1.036954 | 1.447615 | -0.340767 | -0.846170 | -0.515065 | -0.699878 | 0.032355 | 0 | 0 |
| 3 | 0.060782 | -0.392075 | 0.826233 | -0.048480 | 1.789786 | -0.552163 | 0.121028 | -0.111355 | 0.220614 | 1.084098 | 0.073241 | 1.176257 | 0 | 0 |
| 4 | -1.116536 | -0.923742 | -1.238971 | -0.919598 | 0.313068 | -1.160111 | 1.316032 | -0.700013 | -1.600210 | 1.084098 | -1.072155 | 1.270095 | 0 | 0 |
| 5 | -1.082752 | -0.067472 | -1.142511 | -0.923999 | 0.389201 | -1.061180 | 2.067035 | -0.269819 | -1.620482 | -0.468180 | -0.962315 | 1.853511 | 0 | 0 |
| 6 | -1.202528 | -0.776453 | -1.135820 | -0.773268 | -0.409932 | -0.832224 | -0.631931 | -0.119492 | 1.620288 | 0.162930 | -1.063358 | 0.001987 | 0 | 0 |
| 7 | -1.206944 | 0.500703 | -0.243295 | -0.611928 | 0.262760 | -0.855004 | 0.001441 | -1.579225 | 0.245997 | 1.084098 | -1.197067 | -0.681282 | 0 | 0 |
| 8 | -0.982006 | 0.421831 | 0.984997 | -0.550391 | 0.215104 | -1.100712 | 2.248321 | -0.336428 | 1.282109 | 1.084098 | -0.061910 | 0.480931 | 0 | 0 |
| 9 | 1.703175 | 1.029234 | 1.508859 | 0.964653 | 2.164165 | 0.482073 | -0.794175 | 0.729102 | 0.721867 | 0.439251 | 0.198086 | 1.778147 | 0 | 0 |
| 10 | 1.528139 | 0.898498 | 0.923889 | 0.634045 | 2.016059 | 0.674138 | -0.430188 | 0.558129 | 1.200855 | 0.618822 | 0.291110 | 1.853511 | 0 | 0 |
| 11 | 0.334361 | -0.301383 | -0.450307 | -0.470199 | -0.977542 | 0.863046 | -0.396657 | -0.882307 | 0.259614 | 0.809320 | 1.750633 | 0.245556 | 1 | 0 |
| 12 | 0.597458 | 0.773201 | 0.182265 | 0.104921 | 0.580017 | 0.644184 | 1.433111 | 1.735353 | -0.712181 | -0.818426 | -0.129551 | -0.039236 | 0 | 0 |
| 13 | -0.493625 | 1.341798 | -0.632970 | 2.666081 | 2.015672 | 1.537362 | 2.432631 | 0.500840 | 1.337627 | 1.084098 | -0.069418 | 1.097765 | 0 | 0 |
| 14 | 0.962230 | 0.028408 | 0.059003 | -0.233385 | 1.425585 | 1.226062 | 1.160066 | 1.604723 | 0.272753 | 1.084098 | 0.709073 | 1.356660 | 0 | 0 |
| 15 | -0.843247 | 0.160055 | 1.959063 | -0.354971 | -0.294051 | -0.485118 | -0.796417 | -0.212355 | -0.168152 | -0.782723 | -0.232169 | -0.234956 | 0 | 0 |
| 16 | -0.256403 | 1.322075 | 1.520118 | 0.907583 | 1.032003 | 0.577931 | 0.414295 | 0.551597 | 0.783033 | 1.084098 | 0.088744 | 1.848078 | 0 | 0 |
| 17 | -0.456352 | 2.142046 | 1.959063 | 1.734877 | 1.472675 | 0.985568 | 0.646614 | 0.230853 | 0.051480 | -1.122573 | -0.594948 | 0.587848 | 0 | 0 |
| 18 | -0.637040 | -1.030219 | -1.165495 | -0.606800 | 2.164165 | -1.161299 | -0.882343 | 1.727762 | -1.021803 | -1.420384 | -1.197067 | -1.157909 | 0 | 0 |
| 19 | -1.204564 | -1.022455 | -0.683305 | -0.923999 | -0.935167 | -0.988596 | -0.688535 | 1.735353 | -0.372860 | -1.581696 | -1.197067 | -0.315002 | 0 | 0 |
| 20 | -1.077420 | -0.314272 | 1.036699 | -0.850174 | -0.071202 | -1.104286 | 0.102157 | -0.867378 | -0.464391 | 0.591092 | -1.048902 | 1.853510 | 0 | 0 |
| 21 | -0.494728 | -1.023105 | -0.383945 | -0.918858 | 0.498899 | -1.042513 | -0.072226 | -0.187025 | -0.589871 | 1.084098 | -0.986967 | 0.537859 | 0 | 0 |
| 22 | -0.525116 | 0.187277 | 1.220635 | -0.221678 | 0.589822 | -1.115053 | 0.421737 | 0.656325 | -0.098846 | 1.084098 | -0.740109 | 1.853156 | 0 | 0 |
| 23 | -0.282675 | 0.571926 | -0.333097 | 2.860439 | 1.250860 | 1.434107 | 2.006573 | 0.362041 | 1.620288 | 0.896295 | -0.274184 | 1.257508 | 0 | 0 |
| 24 | 1.898264 | 1.240876 | 0.302806 | -0.427292 | -0.709276 | -0.159183 | -0.474972 | 0.273501 | -0.789908 | -0.085745 | 0.868592 | -0.492577 | 1 | 0 |
| 25 | 0.449834 | -0.315494 | -0.187659 | -0.426842 | -0.875213 | 0.916315 | -0.212134 | -0.503325 | 0.251438 | 0.499009 | 1.750633 | 0.286629 | 1 | 0 |
| 26 | -0.745212 | -0.457525 | -0.261214 | 1.653228 | 0.471865 | -0.532478 | 3.048431 | -0.344057 | 0.968569 | -1.690268 | 0.186111 | -0.576484 | 1 | 0 |
| 27 | -1.146960 | 2.737908 | 0.205319 | -0.915593 | 1.224042 | -1.080890 | 2.432039 | -1.475399 | 1.443033 | -0.136290 | -1.161316 | 1.353237 | 0 | 0 |
| 28 | -0.006670 | 2.177984 | -0.364684 | -0.232426 | 2.164165 | -0.501370 | 0.481222 | -0.462802 | -0.495909 | 0.200246 | -0.925963 | -0.127350 | 0 | 0 |
| 29 | -1.198149 | 1.245381 | 1.824748 | -0.544122 | 2.094805 | -0.986543 | 1.141795 | -1.276929 | 1.620288 | 0.772653 | -1.183303 | -0.633811 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | -0.850302 | -0.813520 | -1.238258 | 0.389340 | -0.820553 | -0.147948 | -0.867381 | -1.187728 | 1.620288 | -0.228859 | 0.676545 | -1.166259 | 1 | 1 |
| 226 | 1.113887 | -0.637363 | -0.178882 | -0.867049 | -0.180374 | -0.059762 | -1.031280 | 1.075930 | 1.620288 | -0.831212 | -0.536781 | -0.852284 | 1 | 1 |
| 227 | -0.939216 | -1.028651 | -1.083821 | 0.671288 | -1.104197 | 0.395233 | -0.935964 | -0.545426 | -0.734897 | -1.634988 | 1.750633 | -0.991450 | 1 | 1 |
| 228 | 0.323318 | -0.538560 | 1.959063 | 0.469737 | -0.928167 | 0.315432 | -0.838357 | 0.553622 | -0.985928 | -0.404420 | 0.017683 | -0.868443 | 1 | 1 |
| 229 | 0.651351 | 0.768776 | 1.651644 | 0.613579 | -0.672599 | 0.778338 | -0.288893 | 1.735353 | -0.698435 | -0.803492 | 0.749774 | -0.176628 | 1 | 1 |
| 230 | 1.208263 | 0.590562 | -0.856200 | -0.037362 | -0.890995 | 0.154056 | -0.224548 | 0.060386 | 1.620288 | -0.577590 | 1.081262 | 0.137052 | 1 | 1 |
| 231 | 1.629732 | -0.424079 | -0.793853 | -0.297522 | 0.876511 | -0.502033 | -0.540340 | 1.735353 | 1.179917 | 0.331082 | -0.186926 | 0.043014 | 0 | 1 |
| 232 | 1.097162 | 0.374225 | -0.897801 | -0.315126 | 0.016346 | 0.367805 | -0.397202 | 0.034201 | 0.736455 | 1.084098 | 1.111692 | 0.505493 | 1 | 1 |
| 233 | 1.452404 | -0.818664 | 1.001952 | 0.094142 | -1.157451 | -0.673136 | -1.031650 | -0.681166 | -1.578572 | -1.040029 | 1.750633 | -1.088779 | 1 | 1 |
| 234 | 0.687938 | 0.363684 | -0.077785 | 1.495170 | -0.946513 | 0.162091 | -0.875163 | -0.486609 | -0.657582 | -0.728212 | 1.750633 | 0.166071 | 1 | 1 |
| 235 | -0.511818 | -1.019067 | 1.937312 | -0.923999 | -1.199069 | -1.157422 | 1.424573 | -1.506879 | -0.941792 | 1.084098 | -0.598433 | 1.497327 | 0 | 1 |
| 236 | -0.649452 | 1.110585 | 0.023607 | -0.619494 | -0.351503 | -0.377758 | 0.774664 | -1.037327 | -0.008921 | 1.084098 | -0.300447 | -0.799951 | 0 | 1 |
| 237 | -0.675917 | 0.864345 | 1.959063 | -0.702632 | 0.767520 | -0.242236 | -0.320118 | -0.889868 | -0.543499 | 0.953653 | -0.295863 | 0.184530 | 0 | 1 |
| 238 | -0.820946 | 0.480728 | -0.348445 | 1.706293 | -0.634861 | -0.548325 | -0.658463 | -1.446347 | -0.595881 | -1.346009 | 1.750633 | -0.891882 | 1 | 1 |
| 239 | 0.335654 | -0.570366 | 0.440736 | 2.255028 | -0.777152 | -0.336048 | -0.104033 | 0.504513 | -0.304387 | -0.899407 | 1.750633 | 0.586226 | 1 | 1 |
| 240 | 0.772849 | -0.288034 | 0.998235 | 2.707124 | 0.694491 | 1.600236 | -0.599878 | 0.863164 | 1.620288 | -0.793363 | 1.076398 | 0.313476 | 1 | 1 |
| 241 | -0.725775 | -0.253169 | -1.058923 | -0.019515 | -0.367824 | -0.703472 | 0.163373 | 0.093846 | 1.620288 | 0.712877 | -0.410881 | 0.776774 | 0 | 1 |
| 242 | -0.883133 | 0.370675 | -1.102573 | 0.232760 | -1.047240 | -0.968298 | 0.151912 | -0.346068 | 1.620288 | -0.219994 | -0.255762 | 0.629036 | 0 | 1 |
| 243 | -0.504299 | -0.158035 | -0.129250 | 0.170764 | 0.127833 | -0.424841 | 0.960604 | 0.067663 | 1.620288 | 0.679838 | 0.005478 | 0.547596 | 0 | 1 |
| 244 | -0.829496 | 0.743464 | 1.959063 | 0.266679 | 0.653670 | -1.095468 | 1.900161 | 1.007911 | -0.442746 | 0.870766 | -0.246038 | 1.728101 | 0 | 1 |
| 245 | -1.203285 | 0.455171 | 0.496797 | -0.873183 | 0.629642 | -1.113864 | 1.271226 | 0.542153 | -0.636367 | 1.084098 | -1.140705 | 0.222521 | 0 | 1 |
| 246 | -1.122600 | -0.442839 | 1.824660 | -0.819762 | 1.050840 | -1.048446 | 1.937596 | 0.131208 | -0.474964 | 0.536040 | -0.800922 | 1.853511 | 0 | 1 |
| 247 | -1.206944 | -0.795159 | -1.159900 | -0.187089 | 0.233445 | 2.452569 | 0.183293 | 1.253589 | -1.394630 | 0.753640 | -1.083641 | -1.163597 | 0 | 1 |
| 248 | -0.501382 | -1.030219 | -0.612979 | -0.923999 | -0.608020 | -0.863086 | -0.124132 | -0.944272 | -1.081271 | 1.084098 | -1.170461 | -1.244399 | 0 | 1 |
| 249 | -0.704093 | -0.985145 | 1.199508 | -0.321552 | 2.164165 | -0.193249 | -0.295412 | -0.394034 | 0.032818 | -1.672473 | -0.724683 | -1.067831 | 0 | 1 |
| 250 | 0.831957 | -0.173367 | 1.636565 | 1.345345 | -0.989257 | 0.826135 | -0.824412 | -0.072225 | -0.255975 | -1.005500 | 1.750633 | -1.067130 | 1 | 1 |
| 251 | -0.895156 | -1.022380 | -0.410545 | 2.237273 | -1.199069 | 0.687783 | -1.006468 | -0.694581 | -1.311235 | -1.270219 | 1.750633 | -1.233006 | 1 | 1 |
| 252 | -0.546320 | -0.631883 | -0.800789 | -0.187107 | -1.179353 | 0.464606 | -0.905192 | -0.113592 | 0.167123 | -0.843254 | 1.750633 | -1.148945 | 1 | 1 |
| 253 | -0.591349 | -0.947758 | -0.915262 | -0.579179 | 0.089961 | 0.047756 | -0.905554 | -1.413215 | -1.184716 | 1.084098 | 0.384684 | -1.035788 | 0 | 1 |
| 254 | -0.072947 | -0.912155 | 0.150662 | -0.246506 | -1.198328 | -0.779047 | -1.031650 | 0.022522 | -1.604883 | -1.609572 | 1.750632 | -1.247525 | 1 | 1 |
255 rows × 14 columns
In [442]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[442]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b82d9496a0>
In [443]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[2]))
Gramma¶
ANN¶
In [291]:
X = df_n_ps_std_ch[2]
In [292]:
y = df_n_ps[2]['chosen']
In [293]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [294]:
X_train.shape
Out[294]:
(162, 12)
In [295]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [296]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [297]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [451]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 30), 'learning_rate_init': 0.006, 'max_iter': 20}, que permiten obtener un Accuracy de 75.31% y un Kappa del 13.17
Tiempo total: 26.15 minutos
In [298]:
grid.best_params_={'activation': 'relu', 'hidden_layer_sizes': (30, 30), 'learning_rate_init': 0.006, 'max_iter': 20}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [299]:
input_tensor = Input(shape = (n0,))
In [300]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [301]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [302]:
model.summary()
Model: "model_17" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_18 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_49 (Dense) (None, 30) 390 _________________________________________________________________ dense_50 (Dense) (None, 30) 930 _________________________________________________________________ dense_51 (Dense) (None, 1) 31 ================================================================= Total params: 1,351 Trainable params: 1,351 Non-trainable params: 0 _________________________________________________________________
In [303]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 162 samples, validate on 54 samples Epoch 1/20 162/162 [==============================] - 0s 3ms/step - loss: 0.6482 - accuracy: 0.6049 - val_loss: 0.6607 - val_accuracy: 0.6667 Epoch 2/20 162/162 [==============================] - 0s 117us/step - loss: 0.5950 - accuracy: 0.7222 - val_loss: 0.6863 - val_accuracy: 0.6667 Epoch 3/20 162/162 [==============================] - 0s 123us/step - loss: 0.5654 - accuracy: 0.7222 - val_loss: 0.6563 - val_accuracy: 0.6667 Epoch 4/20 162/162 [==============================] - 0s 117us/step - loss: 0.5451 - accuracy: 0.7222 - val_loss: 0.6296 - val_accuracy: 0.6667 Epoch 5/20 162/162 [==============================] - 0s 117us/step - loss: 0.5314 - accuracy: 0.7222 - val_loss: 0.6051 - val_accuracy: 0.6667 Epoch 6/20 162/162 [==============================] - 0s 130us/step - loss: 0.5334 - accuracy: 0.7346 - val_loss: 0.6051 - val_accuracy: 0.6852 Epoch 7/20 162/162 [==============================] - 0s 136us/step - loss: 0.5214 - accuracy: 0.7407 - val_loss: 0.6138 - val_accuracy: 0.6667 Epoch 8/20 162/162 [==============================] - 0s 117us/step - loss: 0.5048 - accuracy: 0.7222 - val_loss: 0.6388 - val_accuracy: 0.6667 Epoch 9/20 162/162 [==============================] - 0s 130us/step - loss: 0.5033 - accuracy: 0.7284 - val_loss: 0.6578 - val_accuracy: 0.6667 Epoch 10/20 162/162 [==============================] - 0s 123us/step - loss: 0.4883 - accuracy: 0.7284 - val_loss: 0.6463 - val_accuracy: 0.6667 Epoch 11/20 162/162 [==============================] - 0s 105us/step - loss: 0.4795 - accuracy: 0.7407 - val_loss: 0.6296 - val_accuracy: 0.6852 Epoch 12/20 162/162 [==============================] - 0s 105us/step - loss: 0.4644 - accuracy: 0.7407 - val_loss: 0.6182 - val_accuracy: 0.6852 Epoch 13/20 162/162 [==============================] - 0s 99us/step - loss: 0.4544 - accuracy: 0.7407 - val_loss: 0.6273 - val_accuracy: 0.6667 Epoch 14/20 162/162 [==============================] - 0s 111us/step - loss: 0.4402 - accuracy: 0.7716 - val_loss: 0.6358 - val_accuracy: 0.7037 Epoch 15/20 162/162 [==============================] - 0s 99us/step - loss: 0.4355 - accuracy: 0.8025 - val_loss: 0.6463 - val_accuracy: 0.6481 Epoch 16/20 162/162 [==============================] - 0s 99us/step - loss: 0.4332 - accuracy: 0.7901 - val_loss: 0.6489 - val_accuracy: 0.6667 Epoch 17/20 162/162 [==============================] - 0s 99us/step - loss: 0.4194 - accuracy: 0.7963 - val_loss: 0.6496 - val_accuracy: 0.6852 Epoch 18/20 162/162 [==============================] - 0s 117us/step - loss: 0.4019 - accuracy: 0.8086 - val_loss: 0.6535 - val_accuracy: 0.6667 Epoch 19/20 162/162 [==============================] - 0s 99us/step - loss: 0.3849 - accuracy: 0.7963 - val_loss: 0.6350 - val_accuracy: 0.6481 Epoch 20/20 162/162 [==============================] - 0s 111us/step - loss: 0.3683 - accuracy: 0.8148 - val_loss: 0.6268 - val_accuracy: 0.6667
In [304]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 20)
In [305]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
54/54 [==============================] - 0s 111us/step test loss: 0.6267764259267736, test accuracy: 0.6666666865348816
In [306]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.7052469135802469
In [307]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.12903225806451613 [[32 4] [14 4]]
KMeans¶
In [462]:
X
Out[462]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.134448 | -0.644769 | -0.741425 | -0.871269 | 0.189298 | -0.623423 | -0.529336 | 0.357204 | -0.325100 | 0.746582 | -0.476439 | 0.217416 |
| 1 | -0.027862 | 1.690489 | 0.055411 | -0.561004 | 0.495091 | -0.306711 | 0.886175 | -0.561042 | 1.431598 | 0.158038 | 0.235945 | 1.435645 |
| 2 | -0.377201 | 2.125071 | -0.304151 | 2.056775 | -0.998633 | -0.428416 | 1.821160 | -1.229826 | -0.732612 | -1.686385 | -0.297109 | 0.515440 |
| 3 | -0.590231 | 0.143657 | -0.601072 | 1.282246 | -0.578812 | -0.045810 | 1.094766 | 0.365693 | 1.431598 | -0.618841 | 0.417565 | 0.135593 |
| 4 | -0.233782 | 0.378000 | 1.366350 | 0.941326 | -0.898589 | -0.324641 | 1.117103 | 0.022453 | 1.431598 | -0.507270 | 0.477771 | -0.297988 |
| 5 | -1.227009 | 0.847541 | 0.442158 | -0.853849 | 0.281746 | -0.856990 | 1.323879 | -0.878380 | 0.254105 | 1.353628 | -1.133848 | -0.771482 |
| 6 | -0.412709 | 0.592753 | 0.609235 | -0.136011 | 0.556074 | -0.212081 | 0.908709 | 0.462724 | 1.149973 | 1.353628 | -0.748234 | -0.690894 |
| 7 | -0.724090 | 0.958854 | 0.128425 | -0.061221 | 0.393577 | -0.556616 | 0.938013 | 0.500038 | 1.431598 | 0.461706 | -0.450195 | -0.406088 |
| 8 | 2.134447 | -0.911970 | 1.617930 | -0.854742 | -0.491688 | -0.873878 | -0.905227 | 1.840158 | -0.631307 | -0.682701 | -0.960551 | -0.147169 |
| 9 | 2.134447 | -0.392687 | 1.219691 | -0.556103 | -0.426645 | -0.134948 | -0.847940 | 0.441485 | -0.677993 | 0.490526 | -0.789786 | -0.234072 |
| 10 | 2.134447 | -1.196966 | -0.393629 | -1.145266 | -0.441885 | -0.686755 | -1.088890 | -0.281516 | -1.424181 | 0.084569 | -1.220743 | -1.033826 |
| 11 | 1.555294 | -0.387633 | 1.984770 | -0.288986 | -0.227389 | 0.249804 | -0.754853 | 2.092450 | -0.616701 | -0.159824 | -0.411527 | -0.481036 |
| 12 | -0.104131 | -1.361797 | -0.426957 | -1.264951 | -0.817715 | 2.665316 | -1.021180 | -0.205321 | -1.821663 | -0.584671 | -0.883267 | -1.449205 |
| 13 | -0.010446 | -0.441706 | 0.232007 | -0.347890 | -0.731516 | 1.121770 | -0.797580 | 2.092450 | -0.205641 | -0.471642 | 1.220384 | -0.176572 |
| 14 | 0.338185 | 2.125071 | -1.191115 | 0.600768 | -0.845231 | 2.050341 | 1.870414 | -1.054000 | -1.031042 | -1.723459 | 0.138895 | -1.471125 |
| 15 | 0.639533 | 0.029127 | -0.874094 | 0.222996 | -0.545595 | 2.202264 | -0.496728 | -0.667599 | 0.029151 | -1.291591 | 1.713019 | -0.713484 |
| 16 | 1.192505 | 1.901961 | -0.906081 | -0.118624 | -0.698455 | 1.792230 | -0.847558 | -0.879541 | 0.236249 | -1.396153 | 1.713019 | -0.946362 |
| 17 | 1.282493 | -1.425745 | 1.152539 | -1.289274 | 0.653371 | -1.140708 | -0.583855 | 2.092450 | -1.821663 | -1.624757 | -1.464321 | 0.825445 |
| 18 | -0.285085 | -0.286095 | 0.719085 | -0.553923 | -0.239820 | -0.233179 | -0.248394 | 2.092450 | -0.347790 | 0.612253 | 0.820535 | 0.965173 |
| 19 | 2.134447 | -1.173436 | -0.252515 | -0.849286 | 1.426186 | -0.931306 | -0.744081 | 0.963581 | -1.061327 | 0.051966 | -0.724467 | 1.649501 |
| 20 | -0.278722 | 0.441120 | 0.539755 | 0.073408 | 1.245563 | -0.336695 | 0.870546 | 0.829705 | 0.507678 | 1.353628 | 0.336778 | 1.654823 |
| 21 | -0.611002 | -0.704814 | -0.748805 | -0.482771 | 0.602398 | -0.299716 | 0.585889 | 0.316968 | 0.460384 | 1.353628 | 0.031354 | 0.538523 |
| 22 | 0.042172 | 0.682883 | 1.435959 | 0.203185 | 0.579082 | -0.207847 | 1.042158 | 0.812349 | 0.787282 | 1.101013 | -0.163470 | 1.833579 |
| 23 | 2.134448 | 0.023362 | 1.143286 | 0.064566 | 0.079445 | 1.697608 | 0.134204 | 1.514260 | -0.602064 | 0.050733 | 1.157687 | 0.140809 |
| 24 | 0.973235 | -0.202714 | 0.576270 | -0.222220 | -0.676313 | 0.245482 | -0.104773 | 2.092451 | 0.270294 | 0.386750 | 0.176120 | 0.589755 |
| 25 | 2.134447 | -0.628020 | 0.565132 | -0.080497 | 0.062269 | 1.923406 | -0.349591 | 1.577616 | -0.738948 | -0.236942 | 0.974727 | -0.387827 |
| 26 | -0.475449 | -0.272759 | -0.483306 | 2.568625 | -0.432218 | 0.188443 | -0.613425 | -0.934384 | -1.078606 | -1.527243 | 1.713017 | -1.230804 |
| 27 | -1.274193 | -1.259433 | -0.866787 | 0.542028 | -0.775059 | -0.486970 | -0.840968 | 1.043082 | 1.431597 | -0.491858 | -1.133072 | -1.447443 |
| 28 | -0.649286 | 2.125072 | -0.798596 | 0.055357 | 1.448693 | -0.258788 | 1.830622 | -0.960651 | 0.580136 | 0.182651 | -0.817420 | 1.057098 |
| 29 | 0.526348 | 0.918778 | -1.174363 | 0.653683 | -1.116289 | 0.446306 | -0.968896 | -0.898607 | 0.359556 | -1.164241 | 1.713019 | -1.326477 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | -1.319423 | 0.175014 | -1.200954 | -1.285944 | -0.257920 | -1.092671 | -0.865905 | -1.304258 | 1.431598 | 0.180121 | -1.464321 | -1.376110 |
| 187 | -0.108219 | 2.125071 | -0.213805 | -0.324535 | 1.060404 | -0.410798 | 2.017305 | -0.960634 | -0.126219 | 0.768136 | -0.289177 | 0.648942 |
| 188 | 0.564307 | -0.555458 | -0.658358 | 0.591215 | -0.731287 | -0.224320 | -0.776096 | 0.187651 | 1.431598 | -0.214351 | 0.838382 | -0.562694 |
| 189 | 0.641104 | -0.440581 | -0.583337 | 0.310188 | -0.632755 | -0.132319 | -0.662616 | -0.065449 | 1.431598 | -0.606868 | 1.075355 | -0.564249 |
| 190 | 0.573210 | -0.763939 | -0.684625 | 1.454301 | -0.728330 | 0.803913 | -0.748853 | 0.725106 | 1.431598 | -0.372785 | 1.641772 | -0.524504 |
| 191 | 0.308827 | -0.760763 | 0.972410 | -0.682585 | 1.394392 | -0.667061 | 0.263256 | 2.092451 | -1.002578 | 0.003476 | -0.841976 | 0.597412 |
| 192 | 2.058242 | -1.400292 | 0.536272 | -1.288314 | 0.742803 | -1.140708 | -0.707389 | 2.092451 | -1.470650 | 0.803010 | -0.888001 | 1.611969 |
| 193 | 1.113210 | -0.303602 | 1.445305 | -0.282286 | 1.802868 | -0.661326 | -0.240367 | 2.092451 | -0.790224 | 0.079975 | -0.437082 | 1.116475 |
| 194 | -0.450762 | -0.010607 | 2.004804 | -0.654718 | 0.376854 | -0.729410 | -0.771690 | -0.278808 | -0.906778 | 1.353628 | -0.459874 | 1.481964 |
| 195 | -0.185698 | 0.215292 | 1.601736 | -0.053667 | 0.589974 | -0.573847 | 0.000101 | 0.433513 | 0.029899 | 1.101702 | -0.046786 | 1.833579 |
| 196 | -0.759927 | -0.321732 | 1.394992 | -0.641057 | 0.457596 | -0.862435 | -0.236311 | 0.169558 | -0.637600 | 0.890452 | -0.491654 | 1.833579 |
| 197 | 2.134447 | -0.466820 | 0.555283 | -0.588683 | -0.744840 | 0.462895 | -0.666257 | 0.482783 | -0.396687 | 0.433259 | -0.525795 | -0.479641 |
| 198 | 1.112509 | -0.193649 | 0.414393 | 0.029650 | -0.429733 | 0.928074 | -0.345182 | 1.218287 | 0.542487 | 1.353628 | 0.718168 | 0.277470 |
| 199 | 0.905453 | -0.524964 | 1.086044 | -0.043938 | -0.565400 | 0.694749 | -0.521263 | 1.416977 | -0.106187 | 1.353628 | -0.300623 | -0.411341 |
| 200 | -0.899647 | 0.791525 | -0.677180 | 1.189597 | -0.058699 | -0.824832 | -0.542755 | -0.582878 | 1.431598 | -0.375258 | -0.485654 | -0.124219 |
| 201 | -0.733318 | 2.125071 | -0.643560 | 0.518336 | 0.700518 | -0.615231 | 0.605320 | -0.834916 | 0.796712 | 0.392851 | -0.427106 | -0.084736 |
| 202 | -1.324292 | 0.274775 | -1.200954 | 0.489476 | 2.489246 | -1.088788 | 2.630785 | -1.304258 | -0.653881 | -1.056812 | -1.464109 | -0.646684 |
| 203 | -0.765770 | -0.634466 | -0.695396 | -0.104253 | 1.440461 | -0.292064 | -0.160953 | -0.262483 | -0.585612 | 1.353628 | -0.534450 | 0.252552 |
| 204 | -1.298649 | -0.865495 | -1.182382 | -1.154626 | -0.789274 | -1.140708 | 0.189079 | -1.172061 | -0.779701 | 0.811604 | -1.201474 | 1.833579 |
| 205 | -0.432584 | 0.228569 | 0.159869 | -0.537903 | 1.169801 | -0.537451 | 1.083646 | 0.017223 | -0.308391 | 1.353628 | -0.612028 | 0.693463 |
| 206 | -0.098153 | 1.506263 | 0.399753 | -0.402570 | 0.366102 | 0.205628 | 1.813053 | 0.465722 | 1.228385 | 1.353628 | 0.351359 | 0.987155 |
| 207 | -0.684201 | 1.261357 | 0.827726 | -0.398593 | 1.376622 | -0.085790 | 0.419996 | -0.263408 | 0.575117 | 1.353628 | -0.215556 | 0.466853 |
| 208 | 0.057892 | 1.228574 | 0.545400 | -0.211451 | 2.273548 | 0.295789 | 2.675786 | -0.458831 | -0.436474 | 0.159917 | -0.736888 | 0.615728 |
| 209 | 1.529114 | -1.141477 | -0.952529 | -0.312120 | -1.111930 | -0.845550 | -0.966860 | 0.531829 | 1.431598 | -0.891915 | 0.316064 | -1.051750 |
| 210 | 0.792164 | -1.425745 | -1.171065 | 2.751983 | -1.223875 | 0.047688 | -1.104958 | 1.841305 | 0.432920 | -1.723459 | -0.123688 | -1.471125 |
| 211 | 1.033808 | -1.411737 | -0.414286 | 0.062690 | -1.139737 | -0.864229 | -1.024376 | 0.619133 | 1.431598 | -1.515595 | -0.325421 | -1.435723 |
| 212 | 0.856046 | -0.404521 | -0.808726 | 0.490430 | -0.734186 | 0.939368 | -0.730764 | -0.618840 | 1.257115 | -0.514941 | 1.713019 | -0.664148 |
| 213 | 1.074157 | -0.156701 | -0.547902 | 1.101943 | -0.759892 | 0.974616 | -0.748565 | -0.668486 | 1.431598 | 0.078809 | 1.447463 | -0.508043 |
| 214 | -0.466039 | 1.901913 | -0.716582 | -0.377357 | 1.260897 | 0.205794 | 0.988225 | -0.034946 | -0.146391 | 1.353628 | -0.154870 | 0.032420 |
| 215 | -1.281934 | -1.409413 | 1.316491 | -1.289274 | -1.223875 | 2.665316 | -1.067543 | 1.859460 | -1.821663 | 1.125144 | 0.068239 | -1.471125 |
216 rows × 12 columns
In [463]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[463]:
[2592.0, 2036.504626486333, 1690.4433364167144, 1572.8425668108105, 1474.2750884480324, 1381.4716826450067, 1300.6860863710224, 1239.060407813203, 1202.9625977479168, 1129.4403597805695, 1086.8575546528705, 1056.8830460036334, 1009.1121548696354, 981.6337584386087]
In [464]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[464]:
[<matplotlib.lines.Line2D at 0x1b82f456f60>]
K=2
In [465]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[465]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [466]:
kmeans_ch.labels_
Out[466]:
array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1])
In [467]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[467]:
array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1])
In [468]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [469]:
X
Out[469]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.134448 | -0.644769 | -0.741425 | -0.871269 | 0.189298 | -0.623423 | -0.529336 | 0.357204 | -0.325100 | 0.746582 | -0.476439 | 0.217416 | 1 | 0 |
| 1 | -0.027862 | 1.690489 | 0.055411 | -0.561004 | 0.495091 | -0.306711 | 0.886175 | -0.561042 | 1.431598 | 0.158038 | 0.235945 | 1.435645 | 1 | 0 |
| 2 | -0.377201 | 2.125071 | -0.304151 | 2.056775 | -0.998633 | -0.428416 | 1.821160 | -1.229826 | -0.732612 | -1.686385 | -0.297109 | 0.515440 | 0 | 0 |
| 3 | -0.590231 | 0.143657 | -0.601072 | 1.282246 | -0.578812 | -0.045810 | 1.094766 | 0.365693 | 1.431598 | -0.618841 | 0.417565 | 0.135593 | 0 | 0 |
| 4 | -0.233782 | 0.378000 | 1.366350 | 0.941326 | -0.898589 | -0.324641 | 1.117103 | 0.022453 | 1.431598 | -0.507270 | 0.477771 | -0.297988 | 0 | 0 |
| 5 | -1.227009 | 0.847541 | 0.442158 | -0.853849 | 0.281746 | -0.856990 | 1.323879 | -0.878380 | 0.254105 | 1.353628 | -1.133848 | -0.771482 | 1 | 0 |
| 6 | -0.412709 | 0.592753 | 0.609235 | -0.136011 | 0.556074 | -0.212081 | 0.908709 | 0.462724 | 1.149973 | 1.353628 | -0.748234 | -0.690894 | 1 | 0 |
| 7 | -0.724090 | 0.958854 | 0.128425 | -0.061221 | 0.393577 | -0.556616 | 0.938013 | 0.500038 | 1.431598 | 0.461706 | -0.450195 | -0.406088 | 1 | 0 |
| 8 | 2.134447 | -0.911970 | 1.617930 | -0.854742 | -0.491688 | -0.873878 | -0.905227 | 1.840158 | -0.631307 | -0.682701 | -0.960551 | -0.147169 | 1 | 0 |
| 9 | 2.134447 | -0.392687 | 1.219691 | -0.556103 | -0.426645 | -0.134948 | -0.847940 | 0.441485 | -0.677993 | 0.490526 | -0.789786 | -0.234072 | 1 | 0 |
| 10 | 2.134447 | -1.196966 | -0.393629 | -1.145266 | -0.441885 | -0.686755 | -1.088890 | -0.281516 | -1.424181 | 0.084569 | -1.220743 | -1.033826 | 1 | 0 |
| 11 | 1.555294 | -0.387633 | 1.984770 | -0.288986 | -0.227389 | 0.249804 | -0.754853 | 2.092450 | -0.616701 | -0.159824 | -0.411527 | -0.481036 | 1 | 0 |
| 12 | -0.104131 | -1.361797 | -0.426957 | -1.264951 | -0.817715 | 2.665316 | -1.021180 | -0.205321 | -1.821663 | -0.584671 | -0.883267 | -1.449205 | 0 | 0 |
| 13 | -0.010446 | -0.441706 | 0.232007 | -0.347890 | -0.731516 | 1.121770 | -0.797580 | 2.092450 | -0.205641 | -0.471642 | 1.220384 | -0.176572 | 0 | 0 |
| 14 | 0.338185 | 2.125071 | -1.191115 | 0.600768 | -0.845231 | 2.050341 | 1.870414 | -1.054000 | -1.031042 | -1.723459 | 0.138895 | -1.471125 | 0 | 0 |
| 15 | 0.639533 | 0.029127 | -0.874094 | 0.222996 | -0.545595 | 2.202264 | -0.496728 | -0.667599 | 0.029151 | -1.291591 | 1.713019 | -0.713484 | 0 | 0 |
| 16 | 1.192505 | 1.901961 | -0.906081 | -0.118624 | -0.698455 | 1.792230 | -0.847558 | -0.879541 | 0.236249 | -1.396153 | 1.713019 | -0.946362 | 0 | 0 |
| 17 | 1.282493 | -1.425745 | 1.152539 | -1.289274 | 0.653371 | -1.140708 | -0.583855 | 2.092450 | -1.821663 | -1.624757 | -1.464321 | 0.825445 | 1 | 0 |
| 18 | -0.285085 | -0.286095 | 0.719085 | -0.553923 | -0.239820 | -0.233179 | -0.248394 | 2.092450 | -0.347790 | 0.612253 | 0.820535 | 0.965173 | 1 | 0 |
| 19 | 2.134447 | -1.173436 | -0.252515 | -0.849286 | 1.426186 | -0.931306 | -0.744081 | 0.963581 | -1.061327 | 0.051966 | -0.724467 | 1.649501 | 1 | 0 |
| 20 | -0.278722 | 0.441120 | 0.539755 | 0.073408 | 1.245563 | -0.336695 | 0.870546 | 0.829705 | 0.507678 | 1.353628 | 0.336778 | 1.654823 | 1 | 0 |
| 21 | -0.611002 | -0.704814 | -0.748805 | -0.482771 | 0.602398 | -0.299716 | 0.585889 | 0.316968 | 0.460384 | 1.353628 | 0.031354 | 0.538523 | 1 | 0 |
| 22 | 0.042172 | 0.682883 | 1.435959 | 0.203185 | 0.579082 | -0.207847 | 1.042158 | 0.812349 | 0.787282 | 1.101013 | -0.163470 | 1.833579 | 1 | 0 |
| 23 | 2.134448 | 0.023362 | 1.143286 | 0.064566 | 0.079445 | 1.697608 | 0.134204 | 1.514260 | -0.602064 | 0.050733 | 1.157687 | 0.140809 | 0 | 0 |
| 24 | 0.973235 | -0.202714 | 0.576270 | -0.222220 | -0.676313 | 0.245482 | -0.104773 | 2.092451 | 0.270294 | 0.386750 | 0.176120 | 0.589755 | 1 | 0 |
| 25 | 2.134447 | -0.628020 | 0.565132 | -0.080497 | 0.062269 | 1.923406 | -0.349591 | 1.577616 | -0.738948 | -0.236942 | 0.974727 | -0.387827 | 0 | 0 |
| 26 | -0.475449 | -0.272759 | -0.483306 | 2.568625 | -0.432218 | 0.188443 | -0.613425 | -0.934384 | -1.078606 | -1.527243 | 1.713017 | -1.230804 | 0 | 0 |
| 27 | -1.274193 | -1.259433 | -0.866787 | 0.542028 | -0.775059 | -0.486970 | -0.840968 | 1.043082 | 1.431597 | -0.491858 | -1.133072 | -1.447443 | 0 | 0 |
| 28 | -0.649286 | 2.125072 | -0.798596 | 0.055357 | 1.448693 | -0.258788 | 1.830622 | -0.960651 | 0.580136 | 0.182651 | -0.817420 | 1.057098 | 1 | 0 |
| 29 | 0.526348 | 0.918778 | -1.174363 | 0.653683 | -1.116289 | 0.446306 | -0.968896 | -0.898607 | 0.359556 | -1.164241 | 1.713019 | -1.326477 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | -1.319423 | 0.175014 | -1.200954 | -1.285944 | -0.257920 | -1.092671 | -0.865905 | -1.304258 | 1.431598 | 0.180121 | -1.464321 | -1.376110 | 1 | 1 |
| 187 | -0.108219 | 2.125071 | -0.213805 | -0.324535 | 1.060404 | -0.410798 | 2.017305 | -0.960634 | -0.126219 | 0.768136 | -0.289177 | 0.648942 | 1 | 1 |
| 188 | 0.564307 | -0.555458 | -0.658358 | 0.591215 | -0.731287 | -0.224320 | -0.776096 | 0.187651 | 1.431598 | -0.214351 | 0.838382 | -0.562694 | 0 | 1 |
| 189 | 0.641104 | -0.440581 | -0.583337 | 0.310188 | -0.632755 | -0.132319 | -0.662616 | -0.065449 | 1.431598 | -0.606868 | 1.075355 | -0.564249 | 0 | 1 |
| 190 | 0.573210 | -0.763939 | -0.684625 | 1.454301 | -0.728330 | 0.803913 | -0.748853 | 0.725106 | 1.431598 | -0.372785 | 1.641772 | -0.524504 | 0 | 1 |
| 191 | 0.308827 | -0.760763 | 0.972410 | -0.682585 | 1.394392 | -0.667061 | 0.263256 | 2.092451 | -1.002578 | 0.003476 | -0.841976 | 0.597412 | 1 | 1 |
| 192 | 2.058242 | -1.400292 | 0.536272 | -1.288314 | 0.742803 | -1.140708 | -0.707389 | 2.092451 | -1.470650 | 0.803010 | -0.888001 | 1.611969 | 1 | 1 |
| 193 | 1.113210 | -0.303602 | 1.445305 | -0.282286 | 1.802868 | -0.661326 | -0.240367 | 2.092451 | -0.790224 | 0.079975 | -0.437082 | 1.116475 | 1 | 1 |
| 194 | -0.450762 | -0.010607 | 2.004804 | -0.654718 | 0.376854 | -0.729410 | -0.771690 | -0.278808 | -0.906778 | 1.353628 | -0.459874 | 1.481964 | 1 | 1 |
| 195 | -0.185698 | 0.215292 | 1.601736 | -0.053667 | 0.589974 | -0.573847 | 0.000101 | 0.433513 | 0.029899 | 1.101702 | -0.046786 | 1.833579 | 1 | 1 |
| 196 | -0.759927 | -0.321732 | 1.394992 | -0.641057 | 0.457596 | -0.862435 | -0.236311 | 0.169558 | -0.637600 | 0.890452 | -0.491654 | 1.833579 | 1 | 1 |
| 197 | 2.134447 | -0.466820 | 0.555283 | -0.588683 | -0.744840 | 0.462895 | -0.666257 | 0.482783 | -0.396687 | 0.433259 | -0.525795 | -0.479641 | 1 | 1 |
| 198 | 1.112509 | -0.193649 | 0.414393 | 0.029650 | -0.429733 | 0.928074 | -0.345182 | 1.218287 | 0.542487 | 1.353628 | 0.718168 | 0.277470 | 1 | 1 |
| 199 | 0.905453 | -0.524964 | 1.086044 | -0.043938 | -0.565400 | 0.694749 | -0.521263 | 1.416977 | -0.106187 | 1.353628 | -0.300623 | -0.411341 | 1 | 1 |
| 200 | -0.899647 | 0.791525 | -0.677180 | 1.189597 | -0.058699 | -0.824832 | -0.542755 | -0.582878 | 1.431598 | -0.375258 | -0.485654 | -0.124219 | 0 | 1 |
| 201 | -0.733318 | 2.125071 | -0.643560 | 0.518336 | 0.700518 | -0.615231 | 0.605320 | -0.834916 | 0.796712 | 0.392851 | -0.427106 | -0.084736 | 1 | 1 |
| 202 | -1.324292 | 0.274775 | -1.200954 | 0.489476 | 2.489246 | -1.088788 | 2.630785 | -1.304258 | -0.653881 | -1.056812 | -1.464109 | -0.646684 | 1 | 1 |
| 203 | -0.765770 | -0.634466 | -0.695396 | -0.104253 | 1.440461 | -0.292064 | -0.160953 | -0.262483 | -0.585612 | 1.353628 | -0.534450 | 0.252552 | 1 | 1 |
| 204 | -1.298649 | -0.865495 | -1.182382 | -1.154626 | -0.789274 | -1.140708 | 0.189079 | -1.172061 | -0.779701 | 0.811604 | -1.201474 | 1.833579 | 1 | 1 |
| 205 | -0.432584 | 0.228569 | 0.159869 | -0.537903 | 1.169801 | -0.537451 | 1.083646 | 0.017223 | -0.308391 | 1.353628 | -0.612028 | 0.693463 | 1 | 1 |
| 206 | -0.098153 | 1.506263 | 0.399753 | -0.402570 | 0.366102 | 0.205628 | 1.813053 | 0.465722 | 1.228385 | 1.353628 | 0.351359 | 0.987155 | 1 | 1 |
| 207 | -0.684201 | 1.261357 | 0.827726 | -0.398593 | 1.376622 | -0.085790 | 0.419996 | -0.263408 | 0.575117 | 1.353628 | -0.215556 | 0.466853 | 1 | 1 |
| 208 | 0.057892 | 1.228574 | 0.545400 | -0.211451 | 2.273548 | 0.295789 | 2.675786 | -0.458831 | -0.436474 | 0.159917 | -0.736888 | 0.615728 | 1 | 1 |
| 209 | 1.529114 | -1.141477 | -0.952529 | -0.312120 | -1.111930 | -0.845550 | -0.966860 | 0.531829 | 1.431598 | -0.891915 | 0.316064 | -1.051750 | 0 | 1 |
| 210 | 0.792164 | -1.425745 | -1.171065 | 2.751983 | -1.223875 | 0.047688 | -1.104958 | 1.841305 | 0.432920 | -1.723459 | -0.123688 | -1.471125 | 0 | 1 |
| 211 | 1.033808 | -1.411737 | -0.414286 | 0.062690 | -1.139737 | -0.864229 | -1.024376 | 0.619133 | 1.431598 | -1.515595 | -0.325421 | -1.435723 | 0 | 1 |
| 212 | 0.856046 | -0.404521 | -0.808726 | 0.490430 | -0.734186 | 0.939368 | -0.730764 | -0.618840 | 1.257115 | -0.514941 | 1.713019 | -0.664148 | 0 | 1 |
| 213 | 1.074157 | -0.156701 | -0.547902 | 1.101943 | -0.759892 | 0.974616 | -0.748565 | -0.668486 | 1.431598 | 0.078809 | 1.447463 | -0.508043 | 0 | 1 |
| 214 | -0.466039 | 1.901913 | -0.716582 | -0.377357 | 1.260897 | 0.205794 | 0.988225 | -0.034946 | -0.146391 | 1.353628 | -0.154870 | 0.032420 | 1 | 1 |
| 215 | -1.281934 | -1.409413 | 1.316491 | -1.289274 | -1.223875 | 2.665316 | -1.067543 | 1.859460 | -1.821663 | 1.125144 | 0.068239 | -1.471125 | 1 | 1 |
216 rows × 14 columns
In [470]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[470]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b8299f4fd0>
In [471]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[3]))
Hotel Marrakech¶
ANN¶
In [308]:
X = df_n_ps_std_ch[3]
In [309]:
y = df_n_ps[3]['chosen']
In [310]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [311]:
X_train.shape
Out[311]:
(108, 12)
In [312]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [313]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [314]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [479]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.001, 'max_iter': 1000}, que permiten obtener un Accuracy de 74.07% y un Kappa del 46.20
Tiempo total: 21.22 minutos
In [315]:
grid.best_params_={'activation': 'tanh', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.001, 'max_iter': 1000}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [316]:
input_tensor = Input(shape = (n0,))
In [317]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [318]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [319]:
model.summary()
Model: "model_18" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_19 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_52 (Dense) (None, 30) 390 _________________________________________________________________ dense_53 (Dense) (None, 1) 31 ================================================================= Total params: 421 Trainable params: 421 Non-trainable params: 0 _________________________________________________________________
In [320]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 108 samples, validate on 36 samples Epoch 1/1000 108/108 [==============================] - 0s 1ms/step - loss: 0.8036 - accuracy: 0.5278 - val_loss: 0.8239 - val_accuracy: 0.5000 Epoch 2/1000 108/108 [==============================] - 0s 93us/step - loss: 0.7836 - accuracy: 0.5370 - val_loss: 0.8023 - val_accuracy: 0.5000 Epoch 3/1000 108/108 [==============================] - 0s 83us/step - loss: 0.7693 - accuracy: 0.5556 - val_loss: 0.7850 - val_accuracy: 0.5278 Epoch 4/1000 108/108 [==============================] - 0s 93us/step - loss: 0.7543 - accuracy: 0.5648 - val_loss: 0.7700 - val_accuracy: 0.5556 Epoch 5/1000 108/108 [==============================] - 0s 102us/step - loss: 0.7421 - accuracy: 0.5926 - val_loss: 0.7555 - val_accuracy: 0.5278 Epoch 6/1000 108/108 [==============================] - 0s 93us/step - loss: 0.7308 - accuracy: 0.6019 - val_loss: 0.7443 - val_accuracy: 0.5556 Epoch 7/1000 108/108 [==============================] - 0s 83us/step - loss: 0.7202 - accuracy: 0.5833 - val_loss: 0.7343 - val_accuracy: 0.5833 Epoch 8/1000 108/108 [==============================] - 0s 93us/step - loss: 0.7117 - accuracy: 0.5926 - val_loss: 0.7238 - val_accuracy: 0.5833 Epoch 9/1000 108/108 [==============================] - 0s 93us/step - loss: 0.7036 - accuracy: 0.5926 - val_loss: 0.7153 - val_accuracy: 0.6111 Epoch 10/1000 108/108 [==============================] - 0s 74us/step - loss: 0.6970 - accuracy: 0.5741 - val_loss: 0.7072 - val_accuracy: 0.6111 Epoch 11/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6909 - accuracy: 0.5648 - val_loss: 0.7013 - val_accuracy: 0.6111 Epoch 12/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6852 - accuracy: 0.5741 - val_loss: 0.6959 - val_accuracy: 0.6111 Epoch 13/1000 108/108 [==============================] - 0s 74us/step - loss: 0.6810 - accuracy: 0.5741 - val_loss: 0.6913 - val_accuracy: 0.6111 Epoch 14/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6764 - accuracy: 0.5833 - val_loss: 0.6882 - val_accuracy: 0.6111 Epoch 15/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6729 - accuracy: 0.6019 - val_loss: 0.6847 - val_accuracy: 0.6389 Epoch 16/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6694 - accuracy: 0.5926 - val_loss: 0.6827 - val_accuracy: 0.6389 Epoch 17/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6663 - accuracy: 0.5741 - val_loss: 0.6807 - val_accuracy: 0.6389 Epoch 18/1000 108/108 [==============================] - 0s 74us/step - loss: 0.6638 - accuracy: 0.5741 - val_loss: 0.6792 - val_accuracy: 0.6667 Epoch 19/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6612 - accuracy: 0.5833 - val_loss: 0.6784 - val_accuracy: 0.6667 Epoch 20/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6584 - accuracy: 0.5648 - val_loss: 0.6764 - val_accuracy: 0.6667 Epoch 21/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6561 - accuracy: 0.5648 - val_loss: 0.6752 - val_accuracy: 0.6667 Epoch 22/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6545 - accuracy: 0.5741 - val_loss: 0.6738 - val_accuracy: 0.6667 Epoch 23/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6520 - accuracy: 0.5741 - val_loss: 0.6722 - val_accuracy: 0.6944 Epoch 24/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6504 - accuracy: 0.5833 - val_loss: 0.6711 - val_accuracy: 0.6667 Epoch 25/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6490 - accuracy: 0.5741 - val_loss: 0.6704 - val_accuracy: 0.6667 Epoch 26/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6466 - accuracy: 0.5833 - val_loss: 0.6701 - val_accuracy: 0.6667 Epoch 27/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6449 - accuracy: 0.5833 - val_loss: 0.6694 - val_accuracy: 0.6667 Epoch 28/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6431 - accuracy: 0.5741 - val_loss: 0.6670 - val_accuracy: 0.6667 Epoch 29/1000 108/108 [==============================] - 0s 74us/step - loss: 0.6416 - accuracy: 0.5648 - val_loss: 0.6651 - val_accuracy: 0.6667 Epoch 30/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6401 - accuracy: 0.5741 - val_loss: 0.6630 - val_accuracy: 0.6667 Epoch 31/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6382 - accuracy: 0.5648 - val_loss: 0.6618 - val_accuracy: 0.6667 Epoch 32/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6367 - accuracy: 0.5833 - val_loss: 0.6612 - val_accuracy: 0.6667 Epoch 33/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6353 - accuracy: 0.6019 - val_loss: 0.6607 - val_accuracy: 0.6667 Epoch 00033: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 34/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6343 - accuracy: 0.6019 - val_loss: 0.6601 - val_accuracy: 0.6667 Epoch 35/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6337 - accuracy: 0.6019 - val_loss: 0.6598 - val_accuracy: 0.6389 Epoch 36/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6330 - accuracy: 0.6019 - val_loss: 0.6598 - val_accuracy: 0.6389 Epoch 37/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6324 - accuracy: 0.6019 - val_loss: 0.6591 - val_accuracy: 0.6389 Epoch 38/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6317 - accuracy: 0.6111 - val_loss: 0.6580 - val_accuracy: 0.6111 Epoch 39/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6312 - accuracy: 0.6111 - val_loss: 0.6570 - val_accuracy: 0.6111 Epoch 40/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6307 - accuracy: 0.6111 - val_loss: 0.6563 - val_accuracy: 0.6111 Epoch 41/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6301 - accuracy: 0.6111 - val_loss: 0.6557 - val_accuracy: 0.6111 Epoch 42/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6293 - accuracy: 0.6111 - val_loss: 0.6551 - val_accuracy: 0.6111 Epoch 43/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6288 - accuracy: 0.6111 - val_loss: 0.6548 - val_accuracy: 0.6111 Epoch 00043: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 44/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6283 - accuracy: 0.6111 - val_loss: 0.6545 - val_accuracy: 0.6111 Epoch 45/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6280 - accuracy: 0.6111 - val_loss: 0.6543 - val_accuracy: 0.6111 Epoch 46/1000 108/108 [==============================] - ETA: 0s - loss: 0.5806 - accuracy: 0.68 - 0s 111us/step - loss: 0.6277 - accuracy: 0.6111 - val_loss: 0.6540 - val_accuracy: 0.6389 Epoch 47/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6274 - accuracy: 0.6111 - val_loss: 0.6539 - val_accuracy: 0.6389 Epoch 48/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6272 - accuracy: 0.6019 - val_loss: 0.6537 - val_accuracy: 0.6389 Epoch 49/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6270 - accuracy: 0.6204 - val_loss: 0.6535 - val_accuracy: 0.6389 Epoch 50/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6266 - accuracy: 0.6204 - val_loss: 0.6531 - val_accuracy: 0.6389 Epoch 51/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6263 - accuracy: 0.6111 - val_loss: 0.6526 - val_accuracy: 0.6389 Epoch 52/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6261 - accuracy: 0.6111 - val_loss: 0.6521 - val_accuracy: 0.6389 Epoch 53/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6259 - accuracy: 0.6111 - val_loss: 0.6516 - val_accuracy: 0.6389 Epoch 00053: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 54/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6255 - accuracy: 0.6111 - val_loss: 0.6514 - val_accuracy: 0.6389 Epoch 55/1000 108/108 [==============================] - 0s 83us/step - loss: 0.6254 - accuracy: 0.6111 - val_loss: 0.6512 - val_accuracy: 0.6389 Epoch 56/1000 108/108 [==============================] - 0s 74us/step - loss: 0.6253 - accuracy: 0.6111 - val_loss: 0.6510 - val_accuracy: 0.6389 Epoch 57/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6251 - accuracy: 0.6111 - val_loss: 0.6507 - val_accuracy: 0.6389 Epoch 58/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6250 - accuracy: 0.6111 - val_loss: 0.6505 - val_accuracy: 0.6389 Epoch 59/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6248 - accuracy: 0.6111 - val_loss: 0.6504 - val_accuracy: 0.6389 Epoch 60/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6247 - accuracy: 0.6111 - val_loss: 0.6502 - val_accuracy: 0.6389 Epoch 61/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6246 - accuracy: 0.6111 - val_loss: 0.6501 - val_accuracy: 0.6389 Epoch 62/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6244 - accuracy: 0.6204 - val_loss: 0.6498 - val_accuracy: 0.6389 Epoch 63/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6242 - accuracy: 0.6204 - val_loss: 0.6496 - val_accuracy: 0.6389 Epoch 00063: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 64/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6241 - accuracy: 0.6204 - val_loss: 0.6495 - val_accuracy: 0.6389 Epoch 65/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6240 - accuracy: 0.6204 - val_loss: 0.6494 - val_accuracy: 0.6389 Epoch 66/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6239 - accuracy: 0.6204 - val_loss: 0.6493 - val_accuracy: 0.6389 Epoch 67/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6239 - accuracy: 0.6204 - val_loss: 0.6493 - val_accuracy: 0.6389 Epoch 68/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6238 - accuracy: 0.6204 - val_loss: 0.6492 - val_accuracy: 0.6389 Epoch 69/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6237 - accuracy: 0.6204 - val_loss: 0.6491 - val_accuracy: 0.6389 Epoch 70/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6237 - accuracy: 0.6204 - val_loss: 0.6491 - val_accuracy: 0.6389 Epoch 71/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6236 - accuracy: 0.6204 - val_loss: 0.6490 - val_accuracy: 0.6389 Epoch 72/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6235 - accuracy: 0.6204 - val_loss: 0.6490 - val_accuracy: 0.6389 Epoch 73/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6234 - accuracy: 0.6204 - val_loss: 0.6490 - val_accuracy: 0.6389 Epoch 00073: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 74/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6234 - accuracy: 0.6204 - val_loss: 0.6490 - val_accuracy: 0.6389 Epoch 75/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6234 - accuracy: 0.6204 - val_loss: 0.6489 - val_accuracy: 0.6389 Epoch 76/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6233 - accuracy: 0.6204 - val_loss: 0.6489 - val_accuracy: 0.6389 Epoch 77/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6233 - accuracy: 0.6204 - val_loss: 0.6489 - val_accuracy: 0.6389 Epoch 78/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6233 - accuracy: 0.6204 - val_loss: 0.6488 - val_accuracy: 0.6389 Epoch 79/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6232 - accuracy: 0.6204 - val_loss: 0.6488 - val_accuracy: 0.6389 Epoch 80/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6232 - accuracy: 0.6204 - val_loss: 0.6488 - val_accuracy: 0.6389 Epoch 81/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6232 - accuracy: 0.6204 - val_loss: 0.6488 - val_accuracy: 0.6389 Epoch 82/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6231 - accuracy: 0.6204 - val_loss: 0.6488 - val_accuracy: 0.6389 Epoch 83/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6231 - accuracy: 0.6204 - val_loss: 0.6488 - val_accuracy: 0.6389 Epoch 00083: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 84/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6231 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 85/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6230 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 86/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6230 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 87/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6230 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 88/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6230 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 89/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6230 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 90/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6230 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 91/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6229 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 92/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6229 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 93/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6229 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 00093: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 94/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6229 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 95/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6229 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 96/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6229 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 97/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6229 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 98/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6229 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 99/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 100/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6487 - val_accuracy: 0.6389 Epoch 101/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 102/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 103/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00103: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 104/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 105/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 106/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 107/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 108/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 109/1000 108/108 [==============================] - ETA: 0s - loss: 0.6117 - accuracy: 0.65 - 0s 102us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 110/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 111/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 112/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 113/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00113: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 114/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 115/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 116/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 117/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6228 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 118/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 119/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 120/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 121/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 122/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 123/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00123: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07. Epoch 124/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 125/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 126/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 127/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 128/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 129/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 130/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 131/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 132/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 133/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00133: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07. Epoch 134/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 135/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 136/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 137/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 138/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 139/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 140/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 141/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 142/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 143/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00143: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07. Epoch 144/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 145/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 146/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 147/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 148/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 149/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 150/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 151/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 152/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 153/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00153: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07. Epoch 154/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 155/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 156/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 157/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 158/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 159/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 160/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 161/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 162/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 163/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00163: ReduceLROnPlateau reducing learning rate to 6.103515914901436e-08. Epoch 164/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 165/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 166/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 167/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 168/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 169/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 170/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 171/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 172/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 173/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00173: ReduceLROnPlateau reducing learning rate to 3.051757957450718e-08. Epoch 174/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 175/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 176/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 177/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 178/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 179/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 180/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 181/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 182/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 183/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00183: ReduceLROnPlateau reducing learning rate to 1.525878978725359e-08. Epoch 184/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 185/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 186/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 187/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 188/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 189/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 190/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 191/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 192/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 193/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00193: ReduceLROnPlateau reducing learning rate to 7.629394893626795e-09. Epoch 194/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 195/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 196/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 197/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 198/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 199/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 200/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 201/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 202/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 203/1000 108/108 [==============================] - ETA: 0s - loss: 0.6057 - accuracy: 0.59 - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00203: ReduceLROnPlateau reducing learning rate to 3.814697446813398e-09. Epoch 204/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 205/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 206/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 207/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 208/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 209/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 210/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 211/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 212/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 213/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00213: ReduceLROnPlateau reducing learning rate to 1.907348723406699e-09. Epoch 214/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 215/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 216/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 217/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 218/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 219/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 220/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 221/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 222/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 223/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00223: ReduceLROnPlateau reducing learning rate to 9.536743617033494e-10. Epoch 224/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 225/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 226/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 227/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 228/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 229/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 230/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 231/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 232/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 233/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00233: ReduceLROnPlateau reducing learning rate to 4.768371808516747e-10. Epoch 234/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 235/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 236/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 237/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 238/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 239/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 240/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 241/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 242/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 243/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00243: ReduceLROnPlateau reducing learning rate to 2.3841859042583735e-10. Epoch 244/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 245/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 246/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 247/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 248/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 249/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 250/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 251/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 252/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 253/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00253: ReduceLROnPlateau reducing learning rate to 1.1920929521291868e-10. Epoch 254/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 255/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 256/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 257/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 258/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 259/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 260/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 261/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 262/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 263/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00263: ReduceLROnPlateau reducing learning rate to 5.960464760645934e-11. Epoch 264/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 265/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 266/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 267/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 268/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 269/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 270/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 271/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 272/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 273/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00273: ReduceLROnPlateau reducing learning rate to 2.980232380322967e-11. Epoch 274/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 275/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 276/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 277/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 278/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 279/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 280/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 281/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 282/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 283/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00283: ReduceLROnPlateau reducing learning rate to 1.4901161901614834e-11. Epoch 284/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 285/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 286/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 287/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 288/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 289/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 290/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 291/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 292/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 293/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00293: ReduceLROnPlateau reducing learning rate to 7.450580950807417e-12. Epoch 294/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 295/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 296/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 297/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 298/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 299/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 300/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 301/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 302/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 303/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00303: ReduceLROnPlateau reducing learning rate to 3.725290475403709e-12. Epoch 304/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 305/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 306/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 307/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 308/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 309/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 310/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 311/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 312/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 313/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00313: ReduceLROnPlateau reducing learning rate to 1.8626452377018543e-12. Epoch 314/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 315/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 316/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 317/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 318/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 319/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 320/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 321/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 322/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 323/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00323: ReduceLROnPlateau reducing learning rate to 9.313226188509272e-13. Epoch 324/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 325/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 326/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 327/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 328/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 329/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 330/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 331/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 332/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 333/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00333: ReduceLROnPlateau reducing learning rate to 4.656613094254636e-13. Epoch 334/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 335/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 336/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 337/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 338/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 339/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 340/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 341/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 342/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 343/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00343: ReduceLROnPlateau reducing learning rate to 2.328306547127318e-13. Epoch 344/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 345/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 346/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 347/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 348/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 349/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 350/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 351/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 352/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 353/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00353: ReduceLROnPlateau reducing learning rate to 1.164153273563659e-13. Epoch 354/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 355/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 356/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 357/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 358/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 359/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 360/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 361/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 362/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 363/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00363: ReduceLROnPlateau reducing learning rate to 5.820766367818295e-14. Epoch 364/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 365/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 366/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 367/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 368/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 369/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 370/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 371/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 372/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 373/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00373: ReduceLROnPlateau reducing learning rate to 2.9103831839091474e-14. Epoch 374/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 375/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 376/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 377/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 378/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 379/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 380/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 381/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 382/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 383/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00383: ReduceLROnPlateau reducing learning rate to 1.4551915919545737e-14. Epoch 384/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 385/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 386/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 387/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 388/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 389/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 390/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 391/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 392/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 393/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00393: ReduceLROnPlateau reducing learning rate to 7.275957959772868e-15. Epoch 394/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 395/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 396/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 397/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 398/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 399/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 400/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 401/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 402/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 403/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00403: ReduceLROnPlateau reducing learning rate to 3.637978979886434e-15. Epoch 404/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 405/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 406/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 407/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 408/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 409/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 410/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 411/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 412/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 413/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00413: ReduceLROnPlateau reducing learning rate to 1.818989489943217e-15. Epoch 414/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 415/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 416/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 417/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 418/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 419/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 420/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 421/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 422/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 423/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00423: ReduceLROnPlateau reducing learning rate to 9.094947449716085e-16. Epoch 424/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 425/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 426/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 427/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 428/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 429/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 430/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 431/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 432/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 433/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00433: ReduceLROnPlateau reducing learning rate to 4.547473724858043e-16. Epoch 434/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 435/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 436/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 437/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 438/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 439/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 440/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 441/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 442/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 443/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00443: ReduceLROnPlateau reducing learning rate to 2.2737368624290214e-16. Epoch 444/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 445/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 446/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 447/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 448/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 449/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 450/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 451/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 452/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 453/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00453: ReduceLROnPlateau reducing learning rate to 1.1368684312145107e-16. Epoch 454/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 455/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 456/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 457/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 458/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 459/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 460/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 461/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 462/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 463/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00463: ReduceLROnPlateau reducing learning rate to 5.684342156072553e-17. Epoch 464/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 465/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 466/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 467/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 468/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 469/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 470/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 471/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 472/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 473/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00473: ReduceLROnPlateau reducing learning rate to 2.842171078036277e-17. Epoch 474/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 475/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 476/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 477/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 478/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 479/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 480/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 481/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 482/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 483/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00483: ReduceLROnPlateau reducing learning rate to 1.4210855390181384e-17. Epoch 484/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 485/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 486/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 487/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 488/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 489/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 490/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 491/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 492/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 493/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00493: ReduceLROnPlateau reducing learning rate to 7.105427695090692e-18. Epoch 494/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 495/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 496/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 497/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 498/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 499/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 500/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 501/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 502/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 503/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00503: ReduceLROnPlateau reducing learning rate to 3.552713847545346e-18. Epoch 504/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 505/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 506/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 507/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 508/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 509/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 510/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 511/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 512/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 513/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00513: ReduceLROnPlateau reducing learning rate to 1.776356923772673e-18. Epoch 514/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 515/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 516/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 517/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 518/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 519/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 520/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 521/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 522/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 523/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00523: ReduceLROnPlateau reducing learning rate to 8.881784618863365e-19. Epoch 524/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 525/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 526/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 527/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 528/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 529/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 530/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 531/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 532/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 533/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00533: ReduceLROnPlateau reducing learning rate to 4.440892309431682e-19. Epoch 534/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 535/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 536/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 537/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 538/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 539/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 540/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 541/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 542/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 543/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00543: ReduceLROnPlateau reducing learning rate to 2.220446154715841e-19. Epoch 544/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 545/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 546/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 547/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 548/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 549/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 550/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 551/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 552/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 553/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00553: ReduceLROnPlateau reducing learning rate to 1.1102230773579206e-19. Epoch 554/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 555/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 556/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 557/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 558/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 559/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 560/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 561/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 562/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 563/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00563: ReduceLROnPlateau reducing learning rate to 5.551115386789603e-20. Epoch 564/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 565/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 566/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 567/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 568/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 569/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 570/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 571/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 572/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 573/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00573: ReduceLROnPlateau reducing learning rate to 2.7755576933948015e-20. Epoch 574/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 575/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 576/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 577/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 578/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 579/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 580/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 581/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 582/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 583/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00583: ReduceLROnPlateau reducing learning rate to 1.3877788466974007e-20. Epoch 584/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 585/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 586/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 587/1000 108/108 [==============================] - 0s 185us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 588/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 589/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 590/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 591/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 592/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 593/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00593: ReduceLROnPlateau reducing learning rate to 6.938894233487004e-21. Epoch 594/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 595/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 596/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 597/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 598/1000 108/108 [==============================] - 0s 185us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 599/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 600/1000 108/108 [==============================] - 0s 222us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 601/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 602/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 603/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00603: ReduceLROnPlateau reducing learning rate to 3.469447116743502e-21. Epoch 604/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 605/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 606/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 607/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 608/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 609/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 610/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 611/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 612/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 613/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00613: ReduceLROnPlateau reducing learning rate to 1.734723558371751e-21. Epoch 614/1000 108/108 [==============================] - 0s 204us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 615/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 616/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 617/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 618/1000 108/108 [==============================] - 0s 194us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 619/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 620/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 621/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 622/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 623/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00623: ReduceLROnPlateau reducing learning rate to 8.673617791858755e-22. Epoch 624/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 625/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 626/1000 108/108 [==============================] - ETA: 0s - loss: 0.5830 - accuracy: 0.68 - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 627/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 628/1000 108/108 [==============================] - 0s 185us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 629/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 630/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 631/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 632/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 633/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00633: ReduceLROnPlateau reducing learning rate to 4.336808895929377e-22. Epoch 634/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 635/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 636/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 637/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 638/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 639/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 640/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 641/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 642/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 643/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00643: ReduceLROnPlateau reducing learning rate to 2.1684044479646887e-22. Epoch 644/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 645/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 646/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 647/1000 108/108 [==============================] - 0s 416us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 648/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 649/1000 108/108 [==============================] - ETA: 0s - loss: 0.6038 - accuracy: 0.68 - 0s 342us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 650/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 651/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 652/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 653/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00653: ReduceLROnPlateau reducing learning rate to 1.0842022239823443e-22. Epoch 654/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 655/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 656/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 657/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 658/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 659/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 660/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 661/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 662/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 663/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00663: ReduceLROnPlateau reducing learning rate to 5.421011119911722e-23. Epoch 664/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 665/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 666/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 667/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 668/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 669/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 670/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 671/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 672/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 673/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00673: ReduceLROnPlateau reducing learning rate to 2.710505559955861e-23. Epoch 674/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 675/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 676/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 677/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 678/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 679/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 680/1000 108/108 [==============================] - 0s 324us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 681/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 682/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 683/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00683: ReduceLROnPlateau reducing learning rate to 1.3552527799779304e-23. Epoch 684/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 685/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 686/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 687/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 688/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 689/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 690/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 691/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 692/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 693/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00693: ReduceLROnPlateau reducing learning rate to 6.776263899889652e-24. Epoch 694/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 695/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 696/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 697/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 698/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 699/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 700/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 701/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 702/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 703/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00703: ReduceLROnPlateau reducing learning rate to 3.388131949944826e-24. Epoch 704/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 705/1000 108/108 [==============================] - 0s 305us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 706/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 707/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 708/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 709/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 710/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 711/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 712/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 713/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00713: ReduceLROnPlateau reducing learning rate to 1.694065974972413e-24. Epoch 714/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 715/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 716/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 717/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 718/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 719/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 720/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 721/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 722/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 723/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00723: ReduceLROnPlateau reducing learning rate to 8.470329874862065e-25. Epoch 724/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 725/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 726/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 727/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 728/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 729/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 730/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 731/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 732/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 733/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00733: ReduceLROnPlateau reducing learning rate to 4.2351649374310325e-25. Epoch 734/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 735/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 736/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 737/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 738/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 739/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 740/1000 108/108 [==============================] - 0s 185us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 741/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 742/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 743/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00743: ReduceLROnPlateau reducing learning rate to 2.1175824687155163e-25. Epoch 744/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 745/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 746/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 747/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 748/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 749/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 750/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 751/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 752/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 753/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00753: ReduceLROnPlateau reducing learning rate to 1.0587912343577581e-25. Epoch 754/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 755/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 756/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 757/1000 108/108 [==============================] - ETA: 0s - loss: 0.6535 - accuracy: 0.56 - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 758/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 759/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 760/1000 108/108 [==============================] - 0s 194us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 761/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 762/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 763/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00763: ReduceLROnPlateau reducing learning rate to 5.293956171788791e-26. Epoch 764/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 765/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 766/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 767/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 768/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 769/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 770/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 771/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 772/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 773/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00773: ReduceLROnPlateau reducing learning rate to 2.6469780858943953e-26. Epoch 774/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 775/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 776/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 777/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 778/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 779/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 780/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 781/1000 108/108 [==============================] - 0s 185us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 782/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 783/1000 108/108 [==============================] - 0s 315us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00783: ReduceLROnPlateau reducing learning rate to 1.3234890429471977e-26. Epoch 784/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 785/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 786/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 787/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 788/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 789/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 790/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 791/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 792/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 793/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00793: ReduceLROnPlateau reducing learning rate to 6.617445214735988e-27. Epoch 794/1000 108/108 [==============================] - 0s 213us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 795/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 796/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 797/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 798/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 799/1000 108/108 [==============================] - ETA: 0s - loss: 0.5922 - accuracy: 0.62 - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 800/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 801/1000 108/108 [==============================] - 0s 194us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 802/1000 108/108 [==============================] - 0s 176us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 803/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00803: ReduceLROnPlateau reducing learning rate to 3.308722607367994e-27. Epoch 804/1000 108/108 [==============================] - 0s 194us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 805/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 806/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 807/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 808/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 809/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 810/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 811/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 812/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 813/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00813: ReduceLROnPlateau reducing learning rate to 1.654361303683997e-27. Epoch 814/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 815/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 816/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 817/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 818/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 819/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 820/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 821/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 822/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 823/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00823: ReduceLROnPlateau reducing learning rate to 8.271806518419985e-28. Epoch 824/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 825/1000 108/108 [==============================] - 0s 93us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 826/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 827/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 828/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 829/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 830/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 831/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 832/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 833/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00833: ReduceLROnPlateau reducing learning rate to 4.135903259209993e-28. Epoch 834/1000 108/108 [==============================] - ETA: 0s - loss: 0.6172 - accuracy: 0.65 - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 835/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 836/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 837/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 838/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 839/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 840/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 841/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 842/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 843/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00843: ReduceLROnPlateau reducing learning rate to 2.0679516296049964e-28. Epoch 844/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 845/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 846/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 847/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 848/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 849/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 850/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 851/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 852/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 853/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00853: ReduceLROnPlateau reducing learning rate to 1.0339758148024982e-28. Epoch 854/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 855/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 856/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 857/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 858/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 859/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 860/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 861/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 862/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 863/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00863: ReduceLROnPlateau reducing learning rate to 5.169879074012491e-29. Epoch 864/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 865/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 866/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 867/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 868/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 869/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 870/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 871/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 872/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 873/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00873: ReduceLROnPlateau reducing learning rate to 2.5849395370062454e-29. Epoch 874/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 875/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 876/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 877/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 878/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 879/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 880/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 881/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 882/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 883/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00883: ReduceLROnPlateau reducing learning rate to 1.2924697685031227e-29. Epoch 884/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 885/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 886/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 887/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 888/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 889/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 890/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 891/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 892/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 893/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00893: ReduceLROnPlateau reducing learning rate to 6.462348842515614e-30. Epoch 894/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 895/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 896/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 897/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 898/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 899/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 900/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 901/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 902/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 903/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00903: ReduceLROnPlateau reducing learning rate to 3.231174421257807e-30. Epoch 904/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 905/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 906/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 907/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 908/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 909/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 910/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 911/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 912/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 913/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00913: ReduceLROnPlateau reducing learning rate to 1.6155872106289034e-30. Epoch 914/1000 108/108 [==============================] - 0s 185us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 915/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 916/1000 108/108 [==============================] - 0s 157us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 917/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 918/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 919/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 920/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 921/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 922/1000 108/108 [==============================] - 0s 185us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 923/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00923: ReduceLROnPlateau reducing learning rate to 8.077936053144517e-31. Epoch 924/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 925/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 926/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 927/1000 108/108 [==============================] - ETA: 0s - loss: 0.6531 - accuracy: 0.53 - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 928/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 929/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 930/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 931/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 932/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 933/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00933: ReduceLROnPlateau reducing learning rate to 4.0389680265722585e-31. Epoch 934/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 935/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 936/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 937/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 938/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 939/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 940/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 941/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 942/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 943/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00943: ReduceLROnPlateau reducing learning rate to 2.0194840132861292e-31. Epoch 944/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 945/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 946/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 947/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 948/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 949/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 950/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 951/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 952/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 953/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00953: ReduceLROnPlateau reducing learning rate to 1.0097420066430646e-31. Epoch 954/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 955/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 956/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 957/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 958/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 959/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 960/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 961/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 962/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 963/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00963: ReduceLROnPlateau reducing learning rate to 5.048710033215323e-32. Epoch 964/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 965/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 966/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 967/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 968/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 969/1000 108/108 [==============================] - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 970/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 971/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 972/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 973/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00973: ReduceLROnPlateau reducing learning rate to 2.5243550166076616e-32. Epoch 974/1000 108/108 [==============================] - 0s 102us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 975/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 976/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 977/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 978/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 979/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 980/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 981/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 982/1000 108/108 [==============================] - 0s 148us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 983/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00983: ReduceLROnPlateau reducing learning rate to 1.2621775083038308e-32. Epoch 984/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 985/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 986/1000 108/108 [==============================] - ETA: 0s - loss: 0.6357 - accuracy: 0.62 - 0s 167us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 987/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 988/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 989/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 990/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 991/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 992/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 993/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 00993: ReduceLROnPlateau reducing learning rate to 6.310887541519154e-33. Epoch 994/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 995/1000 108/108 [==============================] - 0s 130us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 996/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 997/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 998/1000 108/108 [==============================] - 0s 139us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 999/1000 108/108 [==============================] - 0s 120us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389 Epoch 1000/1000 108/108 [==============================] - 0s 111us/step - loss: 0.6227 - accuracy: 0.6204 - val_loss: 0.6486 - val_accuracy: 0.6389
In [321]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 1000)
In [322]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
36/36 [==============================] - 0s 111us/step test loss: 0.648619532585144, test accuracy: 0.6388888955116272
In [323]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.684375
In [324]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.22516556291390732 [[18 2] [11 5]]
KMeans¶
In [490]:
X
Out[490]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.580880 | -0.990276 | 0.508438 | 0.658218 | 1.668751 | -0.289029 | -0.581158 | 1.476860 | 0.248923 | 1.428276 | -0.870636 | -0.174217 |
| 1 | -0.886270 | 1.680400 | 1.708314 | 0.223308 | 0.928680 | -0.795001 | 0.621735 | -0.834425 | -0.922785 | -0.428866 | -1.169994 | -0.427540 |
| 2 | -0.133384 | 0.811454 | -1.227033 | -0.981611 | -1.054713 | -1.027505 | -0.745868 | 0.198884 | 0.508413 | -1.114895 | -0.312476 | 1.844563 |
| 3 | -0.551071 | 0.259668 | -1.073758 | -0.208557 | 0.002105 | -0.655399 | -0.056661 | -1.206025 | -1.133355 | -1.217881 | -0.812411 | 1.844563 |
| 4 | -1.234016 | -0.118636 | -1.227033 | -0.725200 | -1.112673 | -0.992824 | -0.809889 | -1.428072 | -1.315307 | -1.379943 | -0.903603 | 1.844563 |
| 5 | 1.505233 | 0.156720 | 1.279534 | 0.976338 | 0.335058 | 1.153170 | -0.342400 | -0.203256 | -0.952802 | -0.844743 | 1.303172 | -0.087828 |
| 6 | 1.505233 | -0.276775 | 0.478146 | 0.610910 | 1.357200 | 2.163626 | 0.746385 | -0.601441 | -0.891301 | -0.864699 | 0.723335 | -0.218435 |
| 7 | -1.098308 | 2.406593 | 0.908371 | -0.871045 | 1.373331 | -0.134337 | 2.214860 | -0.690075 | 0.702474 | -0.659995 | -0.671389 | -0.457981 |
| 8 | 1.505233 | 0.396108 | -0.186683 | -0.340118 | -1.050443 | -0.431777 | -0.808361 | -0.443038 | -1.175635 | -1.295109 | 0.226236 | -0.613691 |
| 9 | -0.545948 | 0.718771 | -0.767661 | -0.855150 | 0.352508 | -0.644761 | 0.330241 | 0.685662 | 0.593385 | 1.260081 | 0.858576 | 1.844563 |
| 10 | -0.759467 | 1.402120 | 1.708315 | 1.252125 | 0.573961 | 0.248571 | 1.668183 | -0.726951 | -0.188602 | -0.002148 | -0.093469 | 0.787068 |
| 11 | -0.626015 | 2.019019 | 0.765623 | 1.228928 | 2.278317 | 0.591124 | 2.703718 | -0.377378 | 1.164753 | -0.756752 | -0.671882 | -0.027193 |
| 12 | -0.707480 | 0.753456 | 1.102092 | -0.728084 | 0.729813 | -0.794273 | -0.114544 | 0.663648 | 1.218463 | 1.428276 | 0.637503 | 0.904412 |
| 13 | 0.053762 | 1.374763 | -0.064790 | -0.121229 | -0.205398 | -0.641216 | 1.872586 | -0.187941 | 1.498055 | 0.280727 | 0.185105 | -0.176726 |
| 14 | -1.007854 | 1.946086 | 0.162150 | -0.040380 | 1.209596 | -0.283561 | 2.703718 | -0.479048 | 1.139185 | -0.094942 | -0.241802 | -0.230658 |
| 15 | -1.148500 | -1.038639 | 1.466504 | -0.903609 | 0.186581 | -0.985592 | -0.582354 | 2.493864 | -0.658620 | -0.409259 | -1.142408 | -0.002720 |
| 16 | 0.580515 | 0.408966 | 0.759067 | -1.001060 | 0.697356 | -1.027505 | 0.420872 | -1.192638 | -1.046819 | 1.428276 | -1.213118 | -0.620821 |
| 17 | 0.828864 | -0.024616 | 1.708315 | 0.336834 | 2.119154 | -0.083648 | -0.260916 | 0.125194 | -0.252140 | 0.315916 | -0.554175 | 1.271464 |
| 18 | -1.323765 | -1.071100 | -1.227033 | -1.001060 | -0.802220 | -0.814440 | -0.902727 | -1.012315 | -0.180743 | 1.428276 | -1.211184 | -1.197199 |
| 19 | 1.258380 | -0.980344 | -1.169455 | -0.895307 | 0.784365 | 0.582611 | -0.533001 | 0.319883 | -1.027202 | 1.428276 | -1.014103 | 0.265376 |
| 20 | 0.584754 | 2.257694 | 1.617268 | 2.780448 | 0.474336 | 0.004282 | -0.015547 | -1.203892 | -1.203690 | -1.381076 | -1.170062 | -0.610268 |
| 21 | 0.872730 | 2.337993 | 1.708314 | -0.492713 | -0.703382 | -0.834409 | 0.298125 | -0.068247 | 0.445576 | 1.226208 | -1.003116 | -0.229748 |
| 22 | 1.505233 | -0.325266 | 0.239552 | -0.911432 | 0.097951 | -0.791099 | -0.494947 | 1.831184 | -0.367337 | 0.094547 | -0.372461 | 1.678056 |
| 23 | -1.341382 | 1.475366 | 1.708314 | -0.430421 | -0.137473 | -0.924961 | 0.497402 | -1.323459 | -1.068884 | 1.412572 | -1.166306 | -1.088362 |
| 24 | 1.505233 | -0.058685 | 1.279528 | 0.485516 | -0.774672 | -0.465361 | -0.570279 | -0.818106 | -0.541573 | -0.742412 | -0.782598 | 0.830590 |
| 25 | 1.505233 | -0.199351 | 1.568596 | 1.912238 | -0.689720 | -0.366603 | -0.547507 | 0.350096 | 0.290752 | -0.505127 | 0.813170 | 0.107592 |
| 26 | 1.084848 | -0.023978 | 1.708314 | 2.367620 | -0.203979 | 1.003510 | 0.238525 | 0.369125 | 0.271989 | -0.249002 | -0.148803 | 1.184233 |
| 27 | 0.552717 | 0.139915 | 0.547890 | -0.898140 | 0.438535 | -0.781040 | 0.355213 | 2.187005 | -0.064105 | 0.877837 | -0.435347 | 1.844563 |
| 28 | -0.960017 | -0.983960 | -1.209972 | -0.300931 | -1.083476 | -0.784556 | 0.184926 | -0.959029 | 0.169592 | -0.457636 | 0.416267 | 1.844563 |
| 29 | -0.424404 | 1.136447 | -0.827502 | 1.506323 | -0.630661 | 0.988075 | 1.211921 | -0.324148 | 1.498055 | -0.626101 | 1.321091 | 1.289677 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 114 | 0.974362 | -0.400418 | 0.642886 | -0.973136 | -0.705590 | -0.422351 | -0.641737 | -0.124930 | -0.610955 | 1.428276 | -0.948151 | -1.083631 |
| 115 | 0.284279 | 1.069626 | 0.177590 | 0.656479 | 0.265933 | 1.260127 | 0.211181 | 1.479561 | 1.498055 | -0.348265 | 0.959183 | -0.262467 |
| 116 | 0.401154 | -0.702442 | -1.068462 | -0.829697 | -0.787061 | -0.712610 | 1.069084 | -0.995528 | -0.521572 | 1.092917 | 0.094596 | 1.844563 |
| 117 | -0.392111 | -0.441150 | -0.850725 | -0.730060 | -0.590334 | -0.810149 | -0.085415 | -0.255326 | 0.062980 | 1.428276 | 0.100370 | 1.127543 |
| 118 | -1.167027 | -0.128653 | -0.576682 | -0.857585 | -0.875277 | 0.070585 | 2.703717 | -1.315942 | -1.242368 | -0.230806 | -0.934449 | 0.966999 |
| 119 | -1.453231 | -1.071100 | -1.212986 | -0.993891 | -1.111719 | 0.192760 | -0.824082 | -0.543882 | 1.498055 | -1.097875 | -1.213118 | -1.155045 |
| 120 | -1.054909 | -1.071100 | 0.809406 | -1.001060 | 2.502299 | 1.963690 | -0.902727 | -1.387547 | -1.268177 | 1.428276 | -0.575983 | -0.540965 |
| 121 | -1.120738 | -1.071100 | -0.866114 | -0.865353 | 2.514969 | -1.021752 | -0.889346 | -1.354656 | -1.264228 | 0.155874 | -0.975986 | -1.133896 |
| 122 | -0.737469 | -0.066958 | 1.708314 | 0.402373 | 0.024144 | 0.762426 | -0.634542 | 1.630447 | -0.974569 | 0.125146 | 0.555123 | -0.717956 |
| 123 | 0.442153 | -0.009588 | 1.301573 | 0.189897 | 0.126226 | -0.685488 | -0.235148 | 1.449126 | -0.770793 | 1.428276 | 0.338088 | 0.077417 |
| 124 | 0.565085 | -0.109187 | 1.708314 | -0.270542 | -0.174892 | -0.875584 | -0.663332 | -0.616726 | -1.041169 | 0.001534 | -0.452279 | -0.778143 |
| 125 | 1.007454 | 1.212228 | 1.708315 | 0.655560 | 0.997159 | -0.014497 | 1.717990 | 0.884663 | 0.403666 | 0.774716 | 0.552503 | 1.696155 |
| 126 | 1.289118 | 0.354850 | 0.489878 | 0.491648 | 0.353546 | -0.118255 | 0.937431 | 1.341005 | 1.033868 | 1.428276 | 0.501331 | 1.821758 |
| 127 | 0.028566 | -0.686803 | -0.979367 | -0.791220 | 0.101396 | -0.907919 | -0.345653 | -0.965689 | -0.310560 | 0.141809 | 0.026242 | 1.844563 |
| 128 | 0.678311 | -0.817132 | 1.446362 | 0.622042 | 0.636181 | 0.072008 | -0.069805 | -1.132198 | -1.261564 | -1.058991 | -0.259138 | 1.844563 |
| 129 | -0.163049 | -0.571203 | 1.708314 | 0.661019 | 0.470865 | 0.046292 | -0.374436 | -0.995925 | -1.290693 | -1.171613 | -0.486004 | 1.320617 |
| 130 | -0.679135 | -1.071100 | 1.708314 | 1.187633 | -0.983778 | 0.166812 | -0.902727 | -0.804054 | -1.328500 | -1.408130 | 0.096574 | -1.239435 |
| 131 | -0.204732 | -1.050977 | 0.143360 | -1.000482 | -1.121646 | -0.314462 | -0.902727 | 0.206977 | 0.418859 | 1.428276 | 1.018850 | -0.993493 |
| 132 | -0.966699 | -1.071100 | 1.467246 | 0.266055 | -1.121646 | -0.920149 | -0.902727 | 0.092987 | 0.417098 | 1.428276 | 1.104800 | -1.136694 |
| 133 | 0.013654 | 2.406593 | -0.420557 | -0.020562 | 0.100086 | -0.987233 | -0.324993 | -1.008730 | 0.790985 | 1.379950 | 0.681674 | 0.248593 |
| 134 | 0.770196 | 2.406593 | -0.756682 | -0.547320 | -0.185781 | -0.026969 | -0.879742 | -1.399022 | -0.329956 | 0.189407 | 0.180515 | 1.255243 |
| 135 | 0.316774 | 1.171862 | 0.453349 | 0.986569 | 0.390405 | -0.205625 | 0.010260 | 0.507765 | 1.182053 | 1.363422 | 1.911782 | 1.166586 |
| 136 | 0.384484 | 0.519829 | -0.388746 | 0.502793 | -0.676553 | 1.739251 | -0.282937 | -0.553546 | 0.908221 | 0.045664 | 1.911782 | -0.766194 |
| 137 | -1.084993 | 0.429049 | -0.688647 | -0.802982 | -0.789349 | -0.553963 | 0.156609 | 1.547841 | 1.498055 | 0.119488 | 1.636521 | -0.946370 |
| 138 | 0.980726 | 1.839717 | 1.338253 | 1.957363 | 2.514969 | 0.934743 | -0.841064 | -1.071315 | -1.328500 | -1.401581 | -1.188112 | -1.233483 |
| 139 | -1.071701 | -1.071012 | -0.961961 | -0.905451 | 2.339631 | 0.389698 | 0.308230 | 0.057114 | -0.497016 | 0.228955 | -0.961941 | 1.844563 |
| 140 | -0.415164 | -1.071100 | -1.221501 | -0.693006 | 2.514969 | -1.009569 | 1.155568 | -0.071769 | -1.197183 | -1.374479 | -1.213118 | 0.168259 |
| 141 | -1.200546 | -1.040557 | 0.160549 | -0.991650 | -0.803943 | -0.827445 | 0.797509 | 1.475607 | 0.141845 | 1.428276 | -0.383061 | 0.771996 |
| 142 | 1.505233 | 0.201458 | 0.615256 | -0.813296 | -0.041980 | 1.737065 | -0.019083 | 0.530116 | 0.175767 | 1.099664 | -0.498466 | 0.042633 |
| 143 | -0.236120 | -0.146675 | -0.110274 | 2.342766 | 0.998147 | 2.465626 | -0.067593 | -0.182221 | -0.463531 | -0.779670 | 0.565948 | -0.276677 |
144 rows × 12 columns
In [491]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[491]:
[1728.0, 1478.2106072382894, 1332.479257330826, 1218.449951308628, 1138.0957918080462, 1068.887842720851, 1011.6664613063779, 964.4282803174967, 921.5196331917493, 895.2545968491829, 849.4045014782471, 823.1536172247521, 793.2889327408209, 759.9213103718073]
In [492]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[492]:
[<matplotlib.lines.Line2D at 0x1b83165ae10>]
K=2
In [493]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[493]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [494]:
kmeans_ch.labels_
Out[494]:
array([1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0,
1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,
1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0])
In [495]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[495]:
array([1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0,
1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,
1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0])
In [496]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [497]:
X
Out[497]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.580880 | -0.990276 | 0.508438 | 0.658218 | 1.668751 | -0.289029 | -0.581158 | 1.476860 | 0.248923 | 1.428276 | -0.870636 | -0.174217 | 1 | 0 |
| 1 | -0.886270 | 1.680400 | 1.708314 | 0.223308 | 0.928680 | -0.795001 | 0.621735 | -0.834425 | -0.922785 | -0.428866 | -1.169994 | -0.427540 | 0 | 0 |
| 2 | -0.133384 | 0.811454 | -1.227033 | -0.981611 | -1.054713 | -1.027505 | -0.745868 | 0.198884 | 0.508413 | -1.114895 | -0.312476 | 1.844563 | 1 | 0 |
| 3 | -0.551071 | 0.259668 | -1.073758 | -0.208557 | 0.002105 | -0.655399 | -0.056661 | -1.206025 | -1.133355 | -1.217881 | -0.812411 | 1.844563 | 1 | 0 |
| 4 | -1.234016 | -0.118636 | -1.227033 | -0.725200 | -1.112673 | -0.992824 | -0.809889 | -1.428072 | -1.315307 | -1.379943 | -0.903603 | 1.844563 | 1 | 0 |
| 5 | 1.505233 | 0.156720 | 1.279534 | 0.976338 | 0.335058 | 1.153170 | -0.342400 | -0.203256 | -0.952802 | -0.844743 | 1.303172 | -0.087828 | 0 | 0 |
| 6 | 1.505233 | -0.276775 | 0.478146 | 0.610910 | 1.357200 | 2.163626 | 0.746385 | -0.601441 | -0.891301 | -0.864699 | 0.723335 | -0.218435 | 0 | 0 |
| 7 | -1.098308 | 2.406593 | 0.908371 | -0.871045 | 1.373331 | -0.134337 | 2.214860 | -0.690075 | 0.702474 | -0.659995 | -0.671389 | -0.457981 | 0 | 0 |
| 8 | 1.505233 | 0.396108 | -0.186683 | -0.340118 | -1.050443 | -0.431777 | -0.808361 | -0.443038 | -1.175635 | -1.295109 | 0.226236 | -0.613691 | 1 | 0 |
| 9 | -0.545948 | 0.718771 | -0.767661 | -0.855150 | 0.352508 | -0.644761 | 0.330241 | 0.685662 | 0.593385 | 1.260081 | 0.858576 | 1.844563 | 1 | 0 |
| 10 | -0.759467 | 1.402120 | 1.708315 | 1.252125 | 0.573961 | 0.248571 | 1.668183 | -0.726951 | -0.188602 | -0.002148 | -0.093469 | 0.787068 | 0 | 0 |
| 11 | -0.626015 | 2.019019 | 0.765623 | 1.228928 | 2.278317 | 0.591124 | 2.703718 | -0.377378 | 1.164753 | -0.756752 | -0.671882 | -0.027193 | 0 | 0 |
| 12 | -0.707480 | 0.753456 | 1.102092 | -0.728084 | 0.729813 | -0.794273 | -0.114544 | 0.663648 | 1.218463 | 1.428276 | 0.637503 | 0.904412 | 1 | 0 |
| 13 | 0.053762 | 1.374763 | -0.064790 | -0.121229 | -0.205398 | -0.641216 | 1.872586 | -0.187941 | 1.498055 | 0.280727 | 0.185105 | -0.176726 | 0 | 0 |
| 14 | -1.007854 | 1.946086 | 0.162150 | -0.040380 | 1.209596 | -0.283561 | 2.703718 | -0.479048 | 1.139185 | -0.094942 | -0.241802 | -0.230658 | 0 | 0 |
| 15 | -1.148500 | -1.038639 | 1.466504 | -0.903609 | 0.186581 | -0.985592 | -0.582354 | 2.493864 | -0.658620 | -0.409259 | -1.142408 | -0.002720 | 1 | 0 |
| 16 | 0.580515 | 0.408966 | 0.759067 | -1.001060 | 0.697356 | -1.027505 | 0.420872 | -1.192638 | -1.046819 | 1.428276 | -1.213118 | -0.620821 | 1 | 0 |
| 17 | 0.828864 | -0.024616 | 1.708315 | 0.336834 | 2.119154 | -0.083648 | -0.260916 | 0.125194 | -0.252140 | 0.315916 | -0.554175 | 1.271464 | 0 | 0 |
| 18 | -1.323765 | -1.071100 | -1.227033 | -1.001060 | -0.802220 | -0.814440 | -0.902727 | -1.012315 | -0.180743 | 1.428276 | -1.211184 | -1.197199 | 1 | 0 |
| 19 | 1.258380 | -0.980344 | -1.169455 | -0.895307 | 0.784365 | 0.582611 | -0.533001 | 0.319883 | -1.027202 | 1.428276 | -1.014103 | 0.265376 | 1 | 0 |
| 20 | 0.584754 | 2.257694 | 1.617268 | 2.780448 | 0.474336 | 0.004282 | -0.015547 | -1.203892 | -1.203690 | -1.381076 | -1.170062 | -0.610268 | 0 | 0 |
| 21 | 0.872730 | 2.337993 | 1.708314 | -0.492713 | -0.703382 | -0.834409 | 0.298125 | -0.068247 | 0.445576 | 1.226208 | -1.003116 | -0.229748 | 0 | 0 |
| 22 | 1.505233 | -0.325266 | 0.239552 | -0.911432 | 0.097951 | -0.791099 | -0.494947 | 1.831184 | -0.367337 | 0.094547 | -0.372461 | 1.678056 | 1 | 0 |
| 23 | -1.341382 | 1.475366 | 1.708314 | -0.430421 | -0.137473 | -0.924961 | 0.497402 | -1.323459 | -1.068884 | 1.412572 | -1.166306 | -1.088362 | 1 | 0 |
| 24 | 1.505233 | -0.058685 | 1.279528 | 0.485516 | -0.774672 | -0.465361 | -0.570279 | -0.818106 | -0.541573 | -0.742412 | -0.782598 | 0.830590 | 1 | 0 |
| 25 | 1.505233 | -0.199351 | 1.568596 | 1.912238 | -0.689720 | -0.366603 | -0.547507 | 0.350096 | 0.290752 | -0.505127 | 0.813170 | 0.107592 | 0 | 0 |
| 26 | 1.084848 | -0.023978 | 1.708314 | 2.367620 | -0.203979 | 1.003510 | 0.238525 | 0.369125 | 0.271989 | -0.249002 | -0.148803 | 1.184233 | 0 | 0 |
| 27 | 0.552717 | 0.139915 | 0.547890 | -0.898140 | 0.438535 | -0.781040 | 0.355213 | 2.187005 | -0.064105 | 0.877837 | -0.435347 | 1.844563 | 1 | 0 |
| 28 | -0.960017 | -0.983960 | -1.209972 | -0.300931 | -1.083476 | -0.784556 | 0.184926 | -0.959029 | 0.169592 | -0.457636 | 0.416267 | 1.844563 | 1 | 0 |
| 29 | -0.424404 | 1.136447 | -0.827502 | 1.506323 | -0.630661 | 0.988075 | 1.211921 | -0.324148 | 1.498055 | -0.626101 | 1.321091 | 1.289677 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 114 | 0.974362 | -0.400418 | 0.642886 | -0.973136 | -0.705590 | -0.422351 | -0.641737 | -0.124930 | -0.610955 | 1.428276 | -0.948151 | -1.083631 | 1 | 1 |
| 115 | 0.284279 | 1.069626 | 0.177590 | 0.656479 | 0.265933 | 1.260127 | 0.211181 | 1.479561 | 1.498055 | -0.348265 | 0.959183 | -0.262467 | 0 | 1 |
| 116 | 0.401154 | -0.702442 | -1.068462 | -0.829697 | -0.787061 | -0.712610 | 1.069084 | -0.995528 | -0.521572 | 1.092917 | 0.094596 | 1.844563 | 1 | 1 |
| 117 | -0.392111 | -0.441150 | -0.850725 | -0.730060 | -0.590334 | -0.810149 | -0.085415 | -0.255326 | 0.062980 | 1.428276 | 0.100370 | 1.127543 | 1 | 1 |
| 118 | -1.167027 | -0.128653 | -0.576682 | -0.857585 | -0.875277 | 0.070585 | 2.703717 | -1.315942 | -1.242368 | -0.230806 | -0.934449 | 0.966999 | 1 | 1 |
| 119 | -1.453231 | -1.071100 | -1.212986 | -0.993891 | -1.111719 | 0.192760 | -0.824082 | -0.543882 | 1.498055 | -1.097875 | -1.213118 | -1.155045 | 1 | 1 |
| 120 | -1.054909 | -1.071100 | 0.809406 | -1.001060 | 2.502299 | 1.963690 | -0.902727 | -1.387547 | -1.268177 | 1.428276 | -0.575983 | -0.540965 | 1 | 1 |
| 121 | -1.120738 | -1.071100 | -0.866114 | -0.865353 | 2.514969 | -1.021752 | -0.889346 | -1.354656 | -1.264228 | 0.155874 | -0.975986 | -1.133896 | 1 | 1 |
| 122 | -0.737469 | -0.066958 | 1.708314 | 0.402373 | 0.024144 | 0.762426 | -0.634542 | 1.630447 | -0.974569 | 0.125146 | 0.555123 | -0.717956 | 1 | 1 |
| 123 | 0.442153 | -0.009588 | 1.301573 | 0.189897 | 0.126226 | -0.685488 | -0.235148 | 1.449126 | -0.770793 | 1.428276 | 0.338088 | 0.077417 | 1 | 1 |
| 124 | 0.565085 | -0.109187 | 1.708314 | -0.270542 | -0.174892 | -0.875584 | -0.663332 | -0.616726 | -1.041169 | 0.001534 | -0.452279 | -0.778143 | 1 | 1 |
| 125 | 1.007454 | 1.212228 | 1.708315 | 0.655560 | 0.997159 | -0.014497 | 1.717990 | 0.884663 | 0.403666 | 0.774716 | 0.552503 | 1.696155 | 0 | 1 |
| 126 | 1.289118 | 0.354850 | 0.489878 | 0.491648 | 0.353546 | -0.118255 | 0.937431 | 1.341005 | 1.033868 | 1.428276 | 0.501331 | 1.821758 | 0 | 1 |
| 127 | 0.028566 | -0.686803 | -0.979367 | -0.791220 | 0.101396 | -0.907919 | -0.345653 | -0.965689 | -0.310560 | 0.141809 | 0.026242 | 1.844563 | 1 | 1 |
| 128 | 0.678311 | -0.817132 | 1.446362 | 0.622042 | 0.636181 | 0.072008 | -0.069805 | -1.132198 | -1.261564 | -1.058991 | -0.259138 | 1.844563 | 1 | 1 |
| 129 | -0.163049 | -0.571203 | 1.708314 | 0.661019 | 0.470865 | 0.046292 | -0.374436 | -0.995925 | -1.290693 | -1.171613 | -0.486004 | 1.320617 | 1 | 1 |
| 130 | -0.679135 | -1.071100 | 1.708314 | 1.187633 | -0.983778 | 0.166812 | -0.902727 | -0.804054 | -1.328500 | -1.408130 | 0.096574 | -1.239435 | 1 | 1 |
| 131 | -0.204732 | -1.050977 | 0.143360 | -1.000482 | -1.121646 | -0.314462 | -0.902727 | 0.206977 | 0.418859 | 1.428276 | 1.018850 | -0.993493 | 1 | 1 |
| 132 | -0.966699 | -1.071100 | 1.467246 | 0.266055 | -1.121646 | -0.920149 | -0.902727 | 0.092987 | 0.417098 | 1.428276 | 1.104800 | -1.136694 | 1 | 1 |
| 133 | 0.013654 | 2.406593 | -0.420557 | -0.020562 | 0.100086 | -0.987233 | -0.324993 | -1.008730 | 0.790985 | 1.379950 | 0.681674 | 0.248593 | 0 | 1 |
| 134 | 0.770196 | 2.406593 | -0.756682 | -0.547320 | -0.185781 | -0.026969 | -0.879742 | -1.399022 | -0.329956 | 0.189407 | 0.180515 | 1.255243 | 0 | 1 |
| 135 | 0.316774 | 1.171862 | 0.453349 | 0.986569 | 0.390405 | -0.205625 | 0.010260 | 0.507765 | 1.182053 | 1.363422 | 1.911782 | 1.166586 | 0 | 1 |
| 136 | 0.384484 | 0.519829 | -0.388746 | 0.502793 | -0.676553 | 1.739251 | -0.282937 | -0.553546 | 0.908221 | 0.045664 | 1.911782 | -0.766194 | 0 | 1 |
| 137 | -1.084993 | 0.429049 | -0.688647 | -0.802982 | -0.789349 | -0.553963 | 0.156609 | 1.547841 | 1.498055 | 0.119488 | 1.636521 | -0.946370 | 1 | 1 |
| 138 | 0.980726 | 1.839717 | 1.338253 | 1.957363 | 2.514969 | 0.934743 | -0.841064 | -1.071315 | -1.328500 | -1.401581 | -1.188112 | -1.233483 | 0 | 1 |
| 139 | -1.071701 | -1.071012 | -0.961961 | -0.905451 | 2.339631 | 0.389698 | 0.308230 | 0.057114 | -0.497016 | 0.228955 | -0.961941 | 1.844563 | 1 | 1 |
| 140 | -0.415164 | -1.071100 | -1.221501 | -0.693006 | 2.514969 | -1.009569 | 1.155568 | -0.071769 | -1.197183 | -1.374479 | -1.213118 | 0.168259 | 1 | 1 |
| 141 | -1.200546 | -1.040557 | 0.160549 | -0.991650 | -0.803943 | -0.827445 | 0.797509 | 1.475607 | 0.141845 | 1.428276 | -0.383061 | 0.771996 | 1 | 1 |
| 142 | 1.505233 | 0.201458 | 0.615256 | -0.813296 | -0.041980 | 1.737065 | -0.019083 | 0.530116 | 0.175767 | 1.099664 | -0.498466 | 0.042633 | 0 | 1 |
| 143 | -0.236120 | -0.146675 | -0.110274 | 2.342766 | 0.998147 | 2.465626 | -0.067593 | -0.182221 | -0.463531 | -0.779670 | 0.565948 | -0.276677 | 0 | 1 |
144 rows × 14 columns
In [498]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[498]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b831756ac8>
In [499]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[4]))
Specialized¶
ANN¶
In [25]:
X = df_n_ps_std_ch[4]
In [26]:
y = df_n_ps[4]['chosen']
In [27]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [28]:
X_train.shape
Out[28]:
(164, 12)
In [29]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [30]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [31]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [507]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'logistic', 'hidden_layer_sizes': (20,), 'learning_rate_init': 0.004, 'max_iter': 2000}, que permiten obtener un Accuracy de 73.78% y un Kappa del 46.44
Tiempo total: 24.78 minutos
In [32]:
grid.best_params_={'activation': 'sigmoid', 'hidden_layer_sizes': (20,), 'learning_rate_init': 0.004, 'max_iter': 2000}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [33]:
input_tensor = Input(shape = (n0,))
In [34]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [35]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [36]:
model.summary()
Model: "model_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_1 (Dense) (None, 20) 260 _________________________________________________________________ dense_2 (Dense) (None, 1) 21 ================================================================= Total params: 281 Trainable params: 281 Non-trainable params: 0 _________________________________________________________________
In [37]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 164 samples, validate on 55 samples Epoch 1/2000 164/164 [==============================] - 1s 4ms/step - loss: 0.7436 - accuracy: 0.4146 - val_loss: 0.6989 - val_accuracy: 0.4545 Epoch 2/2000 164/164 [==============================] - 0s 98us/step - loss: 0.7193 - accuracy: 0.4573 - val_loss: 0.6965 - val_accuracy: 0.5091 Epoch 3/2000 164/164 [==============================] - 0s 98us/step - loss: 0.7058 - accuracy: 0.4573 - val_loss: 0.6952 - val_accuracy: 0.4909 Epoch 4/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6958 - accuracy: 0.5244 - val_loss: 0.6950 - val_accuracy: 0.4727 Epoch 5/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6861 - accuracy: 0.5427 - val_loss: 0.6954 - val_accuracy: 0.5273 Epoch 6/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6797 - accuracy: 0.5732 - val_loss: 0.6963 - val_accuracy: 0.5091 Epoch 7/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6731 - accuracy: 0.5854 - val_loss: 0.6976 - val_accuracy: 0.4909 Epoch 8/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6677 - accuracy: 0.6098 - val_loss: 0.7010 - val_accuracy: 0.4909 Epoch 9/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6643 - accuracy: 0.6098 - val_loss: 0.7051 - val_accuracy: 0.5091 Epoch 10/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6619 - accuracy: 0.6220 - val_loss: 0.7080 - val_accuracy: 0.5091 Epoch 11/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6589 - accuracy: 0.6280 - val_loss: 0.7086 - val_accuracy: 0.5091 Epoch 12/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6580 - accuracy: 0.6220 - val_loss: 0.7083 - val_accuracy: 0.5273 Epoch 13/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6557 - accuracy: 0.6159 - val_loss: 0.7093 - val_accuracy: 0.5273 Epoch 14/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6546 - accuracy: 0.6402 - val_loss: 0.7106 - val_accuracy: 0.5273 Epoch 15/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6520 - accuracy: 0.6402 - val_loss: 0.7130 - val_accuracy: 0.5455 Epoch 16/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6498 - accuracy: 0.6524 - val_loss: 0.7159 - val_accuracy: 0.5818 Epoch 17/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6474 - accuracy: 0.6585 - val_loss: 0.7181 - val_accuracy: 0.5818 Epoch 18/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6452 - accuracy: 0.6524 - val_loss: 0.7194 - val_accuracy: 0.5818 Epoch 19/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6436 - accuracy: 0.6585 - val_loss: 0.7203 - val_accuracy: 0.5818 Epoch 20/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6416 - accuracy: 0.6524 - val_loss: 0.7199 - val_accuracy: 0.5636 Epoch 21/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6393 - accuracy: 0.6463 - val_loss: 0.7179 - val_accuracy: 0.5455 Epoch 22/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6373 - accuracy: 0.6463 - val_loss: 0.7153 - val_accuracy: 0.5091 Epoch 23/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6365 - accuracy: 0.6768 - val_loss: 0.7155 - val_accuracy: 0.5091 Epoch 24/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6361 - accuracy: 0.6707 - val_loss: 0.7169 - val_accuracy: 0.5091 Epoch 25/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6344 - accuracy: 0.6707 - val_loss: 0.7197 - val_accuracy: 0.5455 Epoch 26/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6324 - accuracy: 0.6585 - val_loss: 0.7238 - val_accuracy: 0.5091 Epoch 00026: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 27/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6302 - accuracy: 0.6341 - val_loss: 0.7254 - val_accuracy: 0.4909 Epoch 28/2000 164/164 [==============================] - ETA: 0s - loss: 0.6634 - accuracy: 0.53 - 0s 91us/step - loss: 0.6304 - accuracy: 0.6402 - val_loss: 0.7253 - val_accuracy: 0.4909 Epoch 29/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6292 - accuracy: 0.6402 - val_loss: 0.7252 - val_accuracy: 0.5091 Epoch 30/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6283 - accuracy: 0.6524 - val_loss: 0.7253 - val_accuracy: 0.5091 Epoch 31/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6280 - accuracy: 0.6585 - val_loss: 0.7244 - val_accuracy: 0.5273 Epoch 32/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6274 - accuracy: 0.6646 - val_loss: 0.7236 - val_accuracy: 0.5273 Epoch 33/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6268 - accuracy: 0.6646 - val_loss: 0.7238 - val_accuracy: 0.5091 Epoch 34/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6262 - accuracy: 0.6585 - val_loss: 0.7240 - val_accuracy: 0.5091 Epoch 35/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6256 - accuracy: 0.6646 - val_loss: 0.7250 - val_accuracy: 0.5273 Epoch 36/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6253 - accuracy: 0.6585 - val_loss: 0.7253 - val_accuracy: 0.5273 Epoch 00036: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 37/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6248 - accuracy: 0.6524 - val_loss: 0.7263 - val_accuracy: 0.5273 Epoch 38/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6245 - accuracy: 0.6524 - val_loss: 0.7281 - val_accuracy: 0.5455 Epoch 39/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6242 - accuracy: 0.6524 - val_loss: 0.7290 - val_accuracy: 0.5455 Epoch 40/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6241 - accuracy: 0.6646 - val_loss: 0.7298 - val_accuracy: 0.5636 Epoch 41/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6240 - accuracy: 0.6646 - val_loss: 0.7304 - val_accuracy: 0.5636 Epoch 42/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6239 - accuracy: 0.6646 - val_loss: 0.7306 - val_accuracy: 0.5455 Epoch 43/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6244 - accuracy: 0.6768 - val_loss: 0.7313 - val_accuracy: 0.5455 Epoch 44/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6241 - accuracy: 0.6768 - val_loss: 0.7311 - val_accuracy: 0.5455 Epoch 45/2000 164/164 [==============================] - 0s 152us/step - loss: 0.6239 - accuracy: 0.6768 - val_loss: 0.7308 - val_accuracy: 0.5455 Epoch 46/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6233 - accuracy: 0.6646 - val_loss: 0.7314 - val_accuracy: 0.5273 Epoch 00046: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 47/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6225 - accuracy: 0.6646 - val_loss: 0.7317 - val_accuracy: 0.5273 Epoch 48/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6223 - accuracy: 0.6585 - val_loss: 0.7320 - val_accuracy: 0.5273 Epoch 49/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6222 - accuracy: 0.6524 - val_loss: 0.7322 - val_accuracy: 0.5455 Epoch 50/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6218 - accuracy: 0.6463 - val_loss: 0.7323 - val_accuracy: 0.5455 Epoch 51/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6216 - accuracy: 0.6524 - val_loss: 0.7328 - val_accuracy: 0.5273 Epoch 52/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6214 - accuracy: 0.6524 - val_loss: 0.7332 - val_accuracy: 0.5273 Epoch 53/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6212 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 54/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6211 - accuracy: 0.6524 - val_loss: 0.7340 - val_accuracy: 0.5455 Epoch 55/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6211 - accuracy: 0.6585 - val_loss: 0.7340 - val_accuracy: 0.5455 Epoch 56/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6210 - accuracy: 0.6524 - val_loss: 0.7341 - val_accuracy: 0.5455 Epoch 00056: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 57/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6209 - accuracy: 0.6524 - val_loss: 0.7342 - val_accuracy: 0.5455 Epoch 58/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6209 - accuracy: 0.6524 - val_loss: 0.7342 - val_accuracy: 0.5455 Epoch 59/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6208 - accuracy: 0.6585 - val_loss: 0.7343 - val_accuracy: 0.5273 Epoch 60/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6207 - accuracy: 0.6646 - val_loss: 0.7342 - val_accuracy: 0.5273 Epoch 61/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6206 - accuracy: 0.6585 - val_loss: 0.7340 - val_accuracy: 0.5273 Epoch 62/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6205 - accuracy: 0.6585 - val_loss: 0.7340 - val_accuracy: 0.5273 Epoch 63/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6205 - accuracy: 0.6585 - val_loss: 0.7341 - val_accuracy: 0.5273 Epoch 64/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6204 - accuracy: 0.6585 - val_loss: 0.7341 - val_accuracy: 0.5273 Epoch 65/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6203 - accuracy: 0.6524 - val_loss: 0.7341 - val_accuracy: 0.5273 Epoch 66/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6202 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00066: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 67/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6201 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 68/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6201 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 69/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6200 - accuracy: 0.6463 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 70/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6200 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 71/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6199 - accuracy: 0.6463 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 72/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6200 - accuracy: 0.6463 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 73/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6199 - accuracy: 0.6463 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 74/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6198 - accuracy: 0.6463 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 75/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6198 - accuracy: 0.6524 - val_loss: 0.7335 - val_accuracy: 0.5273 Epoch 76/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6198 - accuracy: 0.6463 - val_loss: 0.7335 - val_accuracy: 0.5273 Epoch 00076: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 77/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6198 - accuracy: 0.6463 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 78/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6198 - accuracy: 0.6524 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 79/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6197 - accuracy: 0.6463 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 80/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6197 - accuracy: 0.6463 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 81/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6197 - accuracy: 0.6524 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 82/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6197 - accuracy: 0.6524 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 83/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6197 - accuracy: 0.6524 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 84/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6197 - accuracy: 0.6524 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 85/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6197 - accuracy: 0.6524 - val_loss: 0.7336 - val_accuracy: 0.5273 Epoch 86/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 00086: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 87/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 88/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 89/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 90/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 91/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 92/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7337 - val_accuracy: 0.5273 Epoch 93/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 94/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 95/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 96/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 00096: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 97/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 98/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 99/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 100/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 101/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6196 - accuracy: 0.6524 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 102/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 103/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 104/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 105/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 106/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 00106: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 107/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 108/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 109/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 110/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 111/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 112/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7338 - val_accuracy: 0.5273 Epoch 113/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 114/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 115/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 116/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00116: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 117/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 118/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 119/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 120/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 121/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 122/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 123/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 124/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 125/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 126/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00126: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 127/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 128/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 129/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 130/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 131/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 132/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 133/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 134/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 135/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 136/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00136: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07. Epoch 137/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6585 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 138/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 139/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 140/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 141/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 142/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 143/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 144/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 145/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 146/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00146: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07. Epoch 147/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 148/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 149/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 150/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 151/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 152/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 153/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 154/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 155/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 156/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00156: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07. Epoch 157/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 158/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 159/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 160/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 161/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 162/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 163/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 164/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 165/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 166/2000 164/164 [==============================] - ETA: 0s - loss: 0.6557 - accuracy: 0.62 - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00166: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07. Epoch 167/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 168/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 169/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 170/2000 164/164 [==============================] - 0s 152us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 171/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 172/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 173/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 174/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 175/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 176/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00176: ReduceLROnPlateau reducing learning rate to 6.103515914901436e-08. Epoch 177/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 178/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 179/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 180/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 181/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 182/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 183/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 184/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 185/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 186/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00186: ReduceLROnPlateau reducing learning rate to 3.051757957450718e-08. Epoch 187/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 188/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 189/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 190/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 191/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 192/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 193/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 194/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 195/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 196/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00196: ReduceLROnPlateau reducing learning rate to 1.525878978725359e-08. Epoch 197/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 198/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 199/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 200/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 201/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 202/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 203/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 204/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 205/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 206/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00206: ReduceLROnPlateau reducing learning rate to 7.629394893626795e-09. Epoch 207/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 208/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 209/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 210/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 211/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 212/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 213/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 214/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 215/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 216/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00216: ReduceLROnPlateau reducing learning rate to 3.814697446813398e-09. Epoch 217/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 218/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 219/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 220/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 221/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 222/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 223/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 224/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 225/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 226/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00226: ReduceLROnPlateau reducing learning rate to 1.907348723406699e-09. Epoch 227/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 228/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 229/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 230/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 231/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 232/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 233/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 234/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 235/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 236/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00236: ReduceLROnPlateau reducing learning rate to 9.536743617033494e-10. Epoch 237/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 238/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 239/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 240/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 241/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 242/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 243/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 244/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 245/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 246/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00246: ReduceLROnPlateau reducing learning rate to 4.768371808516747e-10. Epoch 247/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 248/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 249/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 250/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 251/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 252/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 253/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 254/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 255/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 256/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00256: ReduceLROnPlateau reducing learning rate to 2.3841859042583735e-10. Epoch 257/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 258/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 259/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 260/2000 164/164 [==============================] - 0s 152us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 261/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 262/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 263/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 264/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 265/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 266/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00266: ReduceLROnPlateau reducing learning rate to 1.1920929521291868e-10. Epoch 267/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 268/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 269/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 270/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 271/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 272/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 273/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 274/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 275/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 276/2000 164/164 [==============================] - ETA: 0s - loss: 0.6168 - accuracy: 0.71 - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00276: ReduceLROnPlateau reducing learning rate to 5.960464760645934e-11. Epoch 277/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 278/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 279/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 280/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 281/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 282/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 283/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 284/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 285/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 286/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00286: ReduceLROnPlateau reducing learning rate to 2.980232380322967e-11. Epoch 287/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 288/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 289/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 290/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 291/2000 164/164 [==============================] - ETA: 0s - loss: 0.6812 - accuracy: 0.53 - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 292/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 293/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 294/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 295/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 296/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00296: ReduceLROnPlateau reducing learning rate to 1.4901161901614834e-11. Epoch 297/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 298/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 299/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 300/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 301/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 302/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 303/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 304/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 305/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 306/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00306: ReduceLROnPlateau reducing learning rate to 7.450580950807417e-12. Epoch 307/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 308/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 309/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 310/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 311/2000 164/164 [==============================] - 0s 329us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 312/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 313/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 314/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 315/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 316/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00316: ReduceLROnPlateau reducing learning rate to 3.725290475403709e-12. Epoch 317/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 318/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 319/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 320/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 321/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 322/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 323/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 324/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 325/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 326/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00326: ReduceLROnPlateau reducing learning rate to 1.8626452377018543e-12. Epoch 327/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 328/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 329/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 330/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 331/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 332/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 333/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 334/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 335/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 336/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00336: ReduceLROnPlateau reducing learning rate to 9.313226188509272e-13. Epoch 337/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 338/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 339/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 340/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 341/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 342/2000 164/164 [==============================] - 0s 152us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 343/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 344/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 345/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 346/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00346: ReduceLROnPlateau reducing learning rate to 4.656613094254636e-13. Epoch 347/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 348/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 349/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 350/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 351/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 352/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 353/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 354/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 355/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 356/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00356: ReduceLROnPlateau reducing learning rate to 2.328306547127318e-13. Epoch 357/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 358/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 359/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 360/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 361/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 362/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 363/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 364/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 365/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 366/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00366: ReduceLROnPlateau reducing learning rate to 1.164153273563659e-13. Epoch 367/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 368/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 369/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 370/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 371/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 372/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 373/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 374/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 375/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 376/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00376: ReduceLROnPlateau reducing learning rate to 5.820766367818295e-14. Epoch 377/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 378/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 379/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 380/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 381/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 382/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 383/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 384/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 385/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 386/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00386: ReduceLROnPlateau reducing learning rate to 2.9103831839091474e-14. Epoch 387/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 388/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 389/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 390/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 391/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 392/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 393/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 394/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 395/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 396/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00396: ReduceLROnPlateau reducing learning rate to 1.4551915919545737e-14. Epoch 397/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 398/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 399/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 400/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 401/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 402/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 403/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 404/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 405/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 406/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00406: ReduceLROnPlateau reducing learning rate to 7.275957959772868e-15. Epoch 407/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 408/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 409/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 410/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 411/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 412/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 413/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 414/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 415/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 416/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00416: ReduceLROnPlateau reducing learning rate to 3.637978979886434e-15. Epoch 417/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 418/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 419/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 420/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 421/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 422/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 423/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 424/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 425/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 426/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00426: ReduceLROnPlateau reducing learning rate to 1.818989489943217e-15. Epoch 427/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 428/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 429/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 430/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 431/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 432/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 433/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 434/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 435/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 436/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00436: ReduceLROnPlateau reducing learning rate to 9.094947449716085e-16. Epoch 437/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 438/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 439/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 440/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 441/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 442/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 443/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 444/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 445/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 446/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00446: ReduceLROnPlateau reducing learning rate to 4.547473724858043e-16. Epoch 447/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 448/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 449/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 450/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 451/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 452/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 453/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 454/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 455/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 456/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00456: ReduceLROnPlateau reducing learning rate to 2.2737368624290214e-16. Epoch 457/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 458/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 459/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 460/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 461/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 462/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 463/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 464/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 465/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 466/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00466: ReduceLROnPlateau reducing learning rate to 1.1368684312145107e-16. Epoch 467/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 468/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 469/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 470/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 471/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 472/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 473/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 474/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 475/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 476/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00476: ReduceLROnPlateau reducing learning rate to 5.684342156072553e-17. Epoch 477/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 478/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 479/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 480/2000 164/164 [==============================] - ETA: 0s - loss: 0.5786 - accuracy: 0.71 - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 481/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 482/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 483/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 484/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 485/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 486/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00486: ReduceLROnPlateau reducing learning rate to 2.842171078036277e-17. Epoch 487/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 488/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 489/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 490/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 491/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 492/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 493/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 494/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 495/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 496/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00496: ReduceLROnPlateau reducing learning rate to 1.4210855390181384e-17. Epoch 497/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 498/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 499/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 500/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 501/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 502/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 503/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 504/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 505/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 506/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00506: ReduceLROnPlateau reducing learning rate to 7.105427695090692e-18. Epoch 507/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 508/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 509/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 510/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 511/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 512/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 513/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 514/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 515/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 516/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00516: ReduceLROnPlateau reducing learning rate to 3.552713847545346e-18. Epoch 517/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 518/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 519/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 520/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 521/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 522/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 523/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 524/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 525/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 526/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00526: ReduceLROnPlateau reducing learning rate to 1.776356923772673e-18. Epoch 527/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 528/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 529/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 530/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 531/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 532/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 533/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 534/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 535/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 536/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00536: ReduceLROnPlateau reducing learning rate to 8.881784618863365e-19. Epoch 537/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 538/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 539/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 540/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 541/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 542/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 543/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 544/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 545/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 546/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00546: ReduceLROnPlateau reducing learning rate to 4.440892309431682e-19. Epoch 547/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 548/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 549/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 550/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 551/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 552/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 553/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 554/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 555/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 556/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00556: ReduceLROnPlateau reducing learning rate to 2.220446154715841e-19. Epoch 557/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 558/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 559/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 560/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 561/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 562/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 563/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 564/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 565/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 566/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00566: ReduceLROnPlateau reducing learning rate to 1.1102230773579206e-19. Epoch 567/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 568/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 569/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 570/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 571/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 572/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 573/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 574/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 575/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 576/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00576: ReduceLROnPlateau reducing learning rate to 5.551115386789603e-20. Epoch 577/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 578/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 579/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 580/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 581/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 582/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 583/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 584/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 585/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 586/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00586: ReduceLROnPlateau reducing learning rate to 2.7755576933948015e-20. Epoch 587/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 588/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 589/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 590/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 591/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 592/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 593/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 594/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 595/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 596/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00596: ReduceLROnPlateau reducing learning rate to 1.3877788466974007e-20. Epoch 597/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 598/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 599/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 600/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 601/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 602/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 603/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 604/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 605/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 606/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00606: ReduceLROnPlateau reducing learning rate to 6.938894233487004e-21. Epoch 607/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 608/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 609/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 610/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 611/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 612/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 613/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 614/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 615/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 616/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00616: ReduceLROnPlateau reducing learning rate to 3.469447116743502e-21. Epoch 617/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 618/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 619/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 620/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 621/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 622/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 623/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 624/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 625/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 626/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00626: ReduceLROnPlateau reducing learning rate to 1.734723558371751e-21. Epoch 627/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 628/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 629/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 630/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 631/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 632/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 633/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 634/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 635/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 636/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00636: ReduceLROnPlateau reducing learning rate to 8.673617791858755e-22. Epoch 637/2000 164/164 [==============================] - 0s 92us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 638/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 639/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 640/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 641/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 642/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 643/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 644/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 645/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 646/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00646: ReduceLROnPlateau reducing learning rate to 4.336808895929377e-22. Epoch 647/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 648/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 649/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 650/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 651/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 652/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 653/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 654/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 655/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 656/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00656: ReduceLROnPlateau reducing learning rate to 2.1684044479646887e-22. Epoch 657/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 658/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 659/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 660/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 661/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 662/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 663/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 664/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 665/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 666/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00666: ReduceLROnPlateau reducing learning rate to 1.0842022239823443e-22. Epoch 667/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 668/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 669/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 670/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 671/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 672/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 673/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 674/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 675/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 676/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00676: ReduceLROnPlateau reducing learning rate to 5.421011119911722e-23. Epoch 677/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 678/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 679/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 680/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 681/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 682/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 683/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 684/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 685/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 686/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00686: ReduceLROnPlateau reducing learning rate to 2.710505559955861e-23. Epoch 687/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 688/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 689/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 690/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 691/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 692/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 693/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 694/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 695/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 696/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00696: ReduceLROnPlateau reducing learning rate to 1.3552527799779304e-23. Epoch 697/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 698/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 699/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 700/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 701/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 702/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 703/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 704/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 705/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 706/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00706: ReduceLROnPlateau reducing learning rate to 6.776263899889652e-24. Epoch 707/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 708/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 709/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 710/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 711/2000 164/164 [==============================] - ETA: 0s - loss: 0.5574 - accuracy: 0.68 - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 712/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 713/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 714/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 715/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 716/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00716: ReduceLROnPlateau reducing learning rate to 3.388131949944826e-24. Epoch 717/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 718/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 719/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 720/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 721/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 722/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 723/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 724/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 725/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 726/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00726: ReduceLROnPlateau reducing learning rate to 1.694065974972413e-24. Epoch 727/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 728/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 729/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 730/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 731/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 732/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 733/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 734/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 735/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 736/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00736: ReduceLROnPlateau reducing learning rate to 8.470329874862065e-25. Epoch 737/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 738/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 739/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 740/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 741/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 742/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 743/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 744/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 745/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 746/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00746: ReduceLROnPlateau reducing learning rate to 4.2351649374310325e-25. Epoch 747/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 748/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 749/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 750/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 751/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 752/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 753/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 754/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 755/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 756/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00756: ReduceLROnPlateau reducing learning rate to 2.1175824687155163e-25. Epoch 757/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 758/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 759/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 760/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 761/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 762/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 763/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 764/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 765/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 766/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00766: ReduceLROnPlateau reducing learning rate to 1.0587912343577581e-25. Epoch 767/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 768/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 769/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 770/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 771/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 772/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 773/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 774/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 775/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 776/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00776: ReduceLROnPlateau reducing learning rate to 5.293956171788791e-26. Epoch 777/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 778/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 779/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 780/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 781/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 782/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 783/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 784/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 785/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 786/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00786: ReduceLROnPlateau reducing learning rate to 2.6469780858943953e-26. Epoch 787/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 788/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 789/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 790/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 791/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 792/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 793/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 794/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 795/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 796/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00796: ReduceLROnPlateau reducing learning rate to 1.3234890429471977e-26. Epoch 797/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 798/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 799/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 800/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 801/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 802/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 803/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 804/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 805/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 806/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00806: ReduceLROnPlateau reducing learning rate to 6.617445214735988e-27. Epoch 807/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 808/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 809/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 810/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 811/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 812/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 813/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 814/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 815/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 816/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00816: ReduceLROnPlateau reducing learning rate to 3.308722607367994e-27. Epoch 817/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 818/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 819/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 820/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 821/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 822/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 823/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 824/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 825/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 826/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00826: ReduceLROnPlateau reducing learning rate to 1.654361303683997e-27. Epoch 827/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 828/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 829/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 830/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 831/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 832/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 833/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 834/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 835/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 836/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00836: ReduceLROnPlateau reducing learning rate to 8.271806518419985e-28. Epoch 837/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 838/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 839/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 840/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 841/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 842/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 843/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 844/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 845/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 846/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00846: ReduceLROnPlateau reducing learning rate to 4.135903259209993e-28. Epoch 847/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 848/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 849/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 850/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 851/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 852/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 853/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 854/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 855/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 856/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00856: ReduceLROnPlateau reducing learning rate to 2.0679516296049964e-28. Epoch 857/2000 164/164 [==============================] - ETA: 0s - loss: 0.6092 - accuracy: 0.68 - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 858/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 859/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 860/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 861/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 862/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 863/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 864/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 865/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 866/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00866: ReduceLROnPlateau reducing learning rate to 1.0339758148024982e-28. Epoch 867/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 868/2000 164/164 [==============================] - 0s 171us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 869/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 870/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 871/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 872/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 873/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 874/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 875/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 876/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00876: ReduceLROnPlateau reducing learning rate to 5.169879074012491e-29. Epoch 877/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 878/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 879/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 880/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 881/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 882/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 883/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 884/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 885/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 886/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00886: ReduceLROnPlateau reducing learning rate to 2.5849395370062454e-29. Epoch 887/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 888/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 889/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 890/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 891/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 892/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 893/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 894/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 895/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 896/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00896: ReduceLROnPlateau reducing learning rate to 1.2924697685031227e-29. Epoch 897/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 898/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 899/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 900/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 901/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 902/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 903/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 904/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 905/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 906/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00906: ReduceLROnPlateau reducing learning rate to 6.462348842515614e-30. Epoch 907/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 908/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 909/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 910/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 911/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 912/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 913/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 914/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 915/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 916/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00916: ReduceLROnPlateau reducing learning rate to 3.231174421257807e-30. Epoch 917/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 918/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 919/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 920/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 921/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 922/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 923/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 924/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 925/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 926/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00926: ReduceLROnPlateau reducing learning rate to 1.6155872106289034e-30. Epoch 927/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 928/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 929/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 930/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 931/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 932/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 933/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 934/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 935/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 936/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00936: ReduceLROnPlateau reducing learning rate to 8.077936053144517e-31. Epoch 937/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 938/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 939/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 940/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 941/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 942/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 943/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 944/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 945/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 946/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00946: ReduceLROnPlateau reducing learning rate to 4.0389680265722585e-31. Epoch 947/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 948/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 949/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 950/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 951/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 952/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 953/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 954/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 955/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 956/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00956: ReduceLROnPlateau reducing learning rate to 2.0194840132861292e-31. Epoch 957/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 958/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 959/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 960/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 961/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 962/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 963/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 964/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 965/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 966/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00966: ReduceLROnPlateau reducing learning rate to 1.0097420066430646e-31. Epoch 967/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 968/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 969/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 970/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 971/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 972/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 973/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 974/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 975/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 976/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00976: ReduceLROnPlateau reducing learning rate to 5.048710033215323e-32. Epoch 977/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 978/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 979/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 980/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 981/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 982/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 983/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 984/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 985/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 986/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00986: ReduceLROnPlateau reducing learning rate to 2.5243550166076616e-32. Epoch 987/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 988/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 989/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 990/2000 164/164 [==============================] - ETA: 0s - loss: 0.5950 - accuracy: 0.65 - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 991/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 992/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 993/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 994/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 995/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 996/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 00996: ReduceLROnPlateau reducing learning rate to 1.2621775083038308e-32. Epoch 997/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 998/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 999/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1000/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1001/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1002/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1003/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1004/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1005/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1006/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01006: ReduceLROnPlateau reducing learning rate to 6.310887541519154e-33. Epoch 1007/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1008/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1009/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1010/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1011/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1012/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1013/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1014/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1015/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1016/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01016: ReduceLROnPlateau reducing learning rate to 3.155443770759577e-33. Epoch 1017/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1018/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1019/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1020/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1021/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1022/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1023/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1024/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1025/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1026/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01026: ReduceLROnPlateau reducing learning rate to 1.5777218853797885e-33. Epoch 1027/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1028/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1029/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1030/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1031/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1032/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1033/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1034/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1035/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1036/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01036: ReduceLROnPlateau reducing learning rate to 7.888609426898942e-34. Epoch 1037/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1038/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1039/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1040/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1041/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1042/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1043/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1044/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1045/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1046/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01046: ReduceLROnPlateau reducing learning rate to 3.944304713449471e-34. Epoch 1047/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1048/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1049/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1050/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1051/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1052/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1053/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1054/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1055/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1056/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01056: ReduceLROnPlateau reducing learning rate to 1.9721523567247356e-34. Epoch 1057/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1058/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1059/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1060/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1061/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1062/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1063/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1064/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1065/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1066/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01066: ReduceLROnPlateau reducing learning rate to 9.860761783623678e-35. Epoch 1067/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1068/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1069/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1070/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1071/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1072/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1073/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1074/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1075/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1076/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01076: ReduceLROnPlateau reducing learning rate to 4.930380891811839e-35. Epoch 1077/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1078/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1079/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1080/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1081/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1082/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1083/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1084/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1085/2000 164/164 [==============================] - 0s 92us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1086/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01086: ReduceLROnPlateau reducing learning rate to 2.4651904459059195e-35. Epoch 1087/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1088/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1089/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1090/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1091/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1092/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1093/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1094/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1095/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1096/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01096: ReduceLROnPlateau reducing learning rate to 1.2325952229529597e-35. Epoch 1097/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1098/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1099/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1100/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1101/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1102/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1103/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1104/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1105/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1106/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01106: ReduceLROnPlateau reducing learning rate to 6.162976114764799e-36. Epoch 1107/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1108/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1109/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1110/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1111/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1112/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1113/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1114/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1115/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1116/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01116: ReduceLROnPlateau reducing learning rate to 3.0814880573823994e-36. Epoch 1117/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1118/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1119/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1120/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1121/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1122/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1123/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1124/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1125/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1126/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01126: ReduceLROnPlateau reducing learning rate to 1.5407440286911997e-36. Epoch 1127/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1128/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1129/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1130/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1131/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1132/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1133/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1134/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1135/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1136/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01136: ReduceLROnPlateau reducing learning rate to 7.703720143455998e-37. Epoch 1137/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1138/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1139/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1140/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1141/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1142/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1143/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1144/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1145/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1146/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01146: ReduceLROnPlateau reducing learning rate to 3.851860071727999e-37. Epoch 1147/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1148/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1149/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1150/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1151/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1152/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1153/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1154/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1155/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1156/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01156: ReduceLROnPlateau reducing learning rate to 1.9259300358639996e-37. Epoch 1157/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1158/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1159/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1160/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1161/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1162/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1163/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1164/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1165/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1166/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01166: ReduceLROnPlateau reducing learning rate to 9.629650179319998e-38. Epoch 1167/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1168/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1169/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1170/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1171/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1172/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1173/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1174/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1175/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1176/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01176: ReduceLROnPlateau reducing learning rate to 4.814825089659999e-38. Epoch 1177/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1178/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1179/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1180/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1181/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1182/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1183/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1184/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1185/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1186/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01186: ReduceLROnPlateau reducing learning rate to 2.4074125448299995e-38. Epoch 1187/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1188/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1189/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1190/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1191/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1192/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1193/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1194/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1195/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1196/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01196: ReduceLROnPlateau reducing learning rate to 1.2037062724149998e-38. Epoch 1197/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1198/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1199/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1200/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1201/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1202/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1203/2000 164/164 [==============================] - ETA: 0s - loss: 0.6377 - accuracy: 0.62 - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1204/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1205/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1206/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01206: ReduceLROnPlateau reducing learning rate to 6.018531362074999e-39. Epoch 1207/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1208/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1209/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1210/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1211/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1212/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1213/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1214/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1215/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1216/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01216: ReduceLROnPlateau reducing learning rate to 3.0092660313621155e-39. Epoch 1217/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1218/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1219/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1220/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1221/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1222/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1223/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1224/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1225/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1226/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01226: ReduceLROnPlateau reducing learning rate to 1.5046330156810577e-39. Epoch 1227/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1228/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1229/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1230/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1231/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1232/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1233/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1234/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1235/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1236/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01236: ReduceLROnPlateau reducing learning rate to 7.523165078405289e-40. Epoch 1237/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1238/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1239/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1240/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1241/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1242/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1243/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1244/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1245/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1246/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01246: ReduceLROnPlateau reducing learning rate to 3.761582539202644e-40. Epoch 1247/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1248/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1249/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1250/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1251/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1252/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1253/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1254/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1255/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1256/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01256: ReduceLROnPlateau reducing learning rate to 1.880794772847483e-40. Epoch 1257/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1258/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1259/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1260/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1261/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1262/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1263/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1264/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1265/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1266/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01266: ReduceLROnPlateau reducing learning rate to 9.403973864237415e-41. Epoch 1267/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1268/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1269/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1270/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1271/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1272/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1273/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1274/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1275/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1276/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01276: ReduceLROnPlateau reducing learning rate to 4.701986932118707e-41. Epoch 1277/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1278/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1279/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1280/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1281/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1282/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1283/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1284/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1285/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1286/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01286: ReduceLROnPlateau reducing learning rate to 2.3509584335977456e-41. Epoch 1287/2000 164/164 [==============================] - 0s 158us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1288/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1289/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1290/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1291/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1292/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1293/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1294/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1295/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1296/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01296: ReduceLROnPlateau reducing learning rate to 1.1754792167988728e-41. Epoch 1297/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1298/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1299/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1300/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1301/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1302/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1303/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1304/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1305/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1306/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01306: ReduceLROnPlateau reducing learning rate to 5.877045759378283e-42. Epoch 1307/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1308/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1309/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1310/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1311/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1312/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1313/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1314/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1315/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1316/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01316: ReduceLROnPlateau reducing learning rate to 2.9385228796891414e-42. Epoch 1317/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1318/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1319/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1320/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1321/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1322/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1323/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1324/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1325/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1326/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01326: ReduceLROnPlateau reducing learning rate to 1.4692614398445707e-42. Epoch 1327/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1328/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1329/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1330/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1331/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1332/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1333/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1334/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1335/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1336/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01336: ReduceLROnPlateau reducing learning rate to 7.342803953062041e-43. Epoch 1337/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1338/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1339/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1340/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1341/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1342/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1343/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1344/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1345/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1346/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01346: ReduceLROnPlateau reducing learning rate to 3.671401976531021e-43. Epoch 1347/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1348/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1349/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1350/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1351/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1352/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1353/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1354/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1355/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1356/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01356: ReduceLROnPlateau reducing learning rate to 1.8357009882655104e-43. Epoch 1357/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1358/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1359/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1360/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1361/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1362/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1363/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1364/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1365/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1366/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01366: ReduceLROnPlateau reducing learning rate to 9.178504941327552e-44. Epoch 1367/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1368/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1369/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1370/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1371/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1372/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1373/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1374/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1375/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1376/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01376: ReduceLROnPlateau reducing learning rate to 4.624284932271896e-44. Epoch 1377/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1378/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1379/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1380/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1381/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1382/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1383/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1384/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1385/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1386/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01386: ReduceLROnPlateau reducing learning rate to 2.312142466135948e-44. Epoch 1387/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1388/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1389/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1390/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1391/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1392/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1393/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1394/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1395/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1396/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01396: ReduceLROnPlateau reducing learning rate to 1.1210387714598537e-44. Epoch 1397/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1398/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1399/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1400/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1401/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1402/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1403/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1404/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1405/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1406/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01406: ReduceLROnPlateau reducing learning rate to 5.605193857299268e-45. Epoch 1407/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1408/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1409/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1410/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1411/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1412/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1413/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1414/2000 164/164 [==============================] - ETA: 0s - loss: 0.6674 - accuracy: 0.65 - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1415/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1416/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01416: ReduceLROnPlateau reducing learning rate to 2.802596928649634e-45. Epoch 1417/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1418/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1419/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1420/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1421/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1422/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1423/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1424/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1425/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1426/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01426: ReduceLROnPlateau reducing learning rate to 1.401298464324817e-45. Epoch 1427/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1428/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1429/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1430/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1431/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1432/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1433/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1434/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1435/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1436/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 01436: ReduceLROnPlateau reducing learning rate to 7.006492321624085e-46. Epoch 1437/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1438/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1439/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1440/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1441/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1442/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1443/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1444/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1445/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1446/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1447/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1448/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1449/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1450/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1451/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1452/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1453/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1454/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1455/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1456/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1457/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1458/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1459/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1460/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1461/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1462/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1463/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1464/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1465/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1466/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1467/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1468/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1469/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1470/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1471/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1472/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1473/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1474/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1475/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1476/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1477/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1478/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1479/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1480/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1481/2000 164/164 [==============================] - 0s 73us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1482/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1483/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1484/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1485/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1486/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1487/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1488/2000 164/164 [==============================] - ETA: 0s - loss: 0.5645 - accuracy: 0.68 - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1489/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1490/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1491/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1492/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1493/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1494/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1495/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1496/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1497/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1498/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1499/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1500/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1501/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1502/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1503/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1504/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1505/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1506/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1507/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1508/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1509/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1510/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1511/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1512/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1513/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1514/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1515/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1516/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1517/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1518/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1519/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1520/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1521/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1522/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1523/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1524/2000 164/164 [==============================] - ETA: 0s - loss: 0.5285 - accuracy: 0.75 - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1525/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1526/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1527/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1528/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1529/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1530/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1531/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1532/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1533/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1534/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1535/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1536/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1537/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1538/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1539/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1540/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1541/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1542/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1543/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1544/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1545/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1546/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1547/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1548/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1549/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1550/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1551/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1552/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1553/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1554/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1555/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1556/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1557/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1558/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1559/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1560/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1561/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1562/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1563/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1564/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1565/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1566/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1567/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1568/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1569/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1570/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1571/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1572/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1573/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1574/2000 164/164 [==============================] - 0s 158us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1575/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1576/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1577/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1578/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1579/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1580/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1581/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1582/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1583/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1584/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1585/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1586/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1587/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1588/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1589/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1590/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1591/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1592/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1593/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1594/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1595/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1596/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1597/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1598/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1599/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1600/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1601/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1602/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1603/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1604/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1605/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1606/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1607/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1608/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1609/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1610/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1611/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1612/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1613/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1614/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1615/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1616/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1617/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1618/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1619/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1620/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1621/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1622/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1623/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1624/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1625/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1626/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1627/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1628/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1629/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1630/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1631/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1632/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1633/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1634/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1635/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1636/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1637/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1638/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1639/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1640/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1641/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1642/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1643/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1644/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1645/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1646/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1647/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1648/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1649/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1650/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1651/2000 164/164 [==============================] - 0s 79us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1652/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1653/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1654/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1655/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1656/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1657/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1658/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1659/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1660/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1661/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1662/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1663/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1664/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1665/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1666/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1667/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1668/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1669/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1670/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1671/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1672/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1673/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1674/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1675/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1676/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1677/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1678/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1679/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1680/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1681/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1682/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1683/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1684/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1685/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1686/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1687/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1688/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1689/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1690/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1691/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1692/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1693/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1694/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1695/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1696/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1697/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1698/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1699/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1700/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1701/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1702/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1703/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1704/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1705/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1706/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1707/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1708/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1709/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1710/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1711/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1712/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1713/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1714/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1715/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1716/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1717/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1718/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1719/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1720/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1721/2000 164/164 [==============================] - 0s 158us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1722/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1723/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1724/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1725/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1726/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1727/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1728/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1729/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1730/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1731/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1732/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1733/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1734/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1735/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1736/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1737/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1738/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1739/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1740/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1741/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1742/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1743/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1744/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1745/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1746/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1747/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1748/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1749/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1750/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1751/2000 164/164 [==============================] - ETA: 0s - loss: 0.5969 - accuracy: 0.65 - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1752/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1753/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1754/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1755/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1756/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1757/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1758/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1759/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1760/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1761/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1762/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1763/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1764/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1765/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1766/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1767/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1768/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1769/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1770/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1771/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1772/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1773/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1774/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1775/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1776/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1777/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1778/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1779/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1780/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1781/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1782/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1783/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1784/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1785/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1786/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1787/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1788/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1789/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1790/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1791/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1792/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1793/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1794/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1795/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1796/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1797/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1798/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1799/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1800/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1801/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1802/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1803/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1804/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1805/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1806/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1807/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1808/2000 164/164 [==============================] - 0s 146us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1809/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1810/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1811/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1812/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1813/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1814/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1815/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1816/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1817/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1818/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1819/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1820/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1821/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1822/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1823/2000 164/164 [==============================] - 0s 128us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1824/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1825/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1826/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1827/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1828/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1829/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1830/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1831/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1832/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1833/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1834/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1835/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1836/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1837/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1838/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1839/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1840/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1841/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1842/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1843/2000 164/164 [==============================] - ETA: 0s - loss: 0.6190 - accuracy: 0.65 - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1844/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1845/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1846/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1847/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1848/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1849/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1850/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1851/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1852/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1853/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1854/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1855/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1856/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1857/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1858/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1859/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1860/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1861/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1862/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1863/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1864/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1865/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1866/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1867/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1868/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1869/2000 164/164 [==============================] - 0s 140us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1870/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1871/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1872/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1873/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1874/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1875/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1876/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1877/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1878/2000 164/164 [==============================] - ETA: 0s - loss: 0.6782 - accuracy: 0.53 - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1879/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1880/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1881/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1882/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1883/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1884/2000 164/164 [==============================] - ETA: 0s - loss: 0.5652 - accuracy: 0.71 - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1885/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1886/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1887/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1888/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1889/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1890/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1891/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1892/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1893/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1894/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1895/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1896/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1897/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1898/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1899/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1900/2000 164/164 [==============================] - 0s 122us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1901/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1902/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1903/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1904/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1905/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1906/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1907/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1908/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1909/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1910/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1911/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1912/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1913/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1914/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1915/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1916/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1917/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1918/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1919/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1920/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1921/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1922/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1923/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1924/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1925/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1926/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1927/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1928/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1929/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1930/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1931/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1932/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1933/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1934/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1935/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1936/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1937/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1938/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1939/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1940/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1941/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1942/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1943/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1944/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1945/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1946/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1947/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1948/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1949/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1950/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1951/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1952/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1953/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1954/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1955/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1956/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1957/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1958/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1959/2000 164/164 [==============================] - 0s 116us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1960/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1961/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1962/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1963/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1964/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1965/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1966/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1967/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1968/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1969/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1970/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1971/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1972/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1973/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1974/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1975/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1976/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1977/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1978/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1979/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1980/2000 164/164 [==============================] - 0s 134us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1981/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1982/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1983/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1984/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1985/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1986/2000 164/164 [==============================] - 0s 110us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1987/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1988/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1989/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1990/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1991/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1992/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1993/2000 164/164 [==============================] - 0s 98us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1994/2000 164/164 [==============================] - 0s 85us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1995/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1996/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1997/2000 164/164 [==============================] - 0s 91us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1998/2000 164/164 [==============================] - 0s 97us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 1999/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273 Epoch 2000/2000 164/164 [==============================] - 0s 104us/step - loss: 0.6195 - accuracy: 0.6524 - val_loss: 0.7339 - val_accuracy: 0.5273
In [38]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 2000)
In [39]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
55/55 [==============================] - 0s 91us/step test loss: 0.7338643464175137, test accuracy: 0.5272727012634277
In [40]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.548941798941799
In [41]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.0542328042328043 [[15 13] [13 14]]
KMeans¶
In [518]:
X
Out[518]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.752761 | -1.114598 | -0.348132 | 2.966304 | -0.925235 | 0.552064 | -0.710296 | 0.412009 | -0.784684 | -1.667162 | -0.834151 | -1.566379 |
| 1 | -1.387006 | -0.333039 | 0.041297 | -0.917052 | 0.656635 | -1.022407 | -0.805166 | -0.905135 | -0.708805 | 1.299875 | -0.948816 | -1.413196 |
| 2 | -0.628834 | 2.234144 | 0.613536 | -0.978644 | 0.836157 | -0.735689 | -0.059767 | -1.571350 | 1.278264 | -1.103616 | -1.153426 | 0.685062 |
| 3 | 0.081693 | 1.765530 | -0.365668 | 0.759057 | -1.136519 | -0.071939 | -0.412587 | -1.310708 | 1.465231 | -1.266573 | 0.040076 | -0.308065 |
| 4 | 0.056206 | 1.646501 | 0.508800 | 0.525847 | 0.506842 | -0.390517 | -0.241209 | -0.409725 | 1.465231 | -0.148455 | -0.354779 | 0.188297 |
| 5 | -0.475284 | 0.673200 | -1.077774 | 0.360339 | -0.032017 | 0.910768 | 1.405699 | -0.748908 | 1.465231 | -0.933838 | -0.225835 | -0.922170 |
| 6 | 0.163433 | -0.011304 | -1.057752 | 1.128932 | 0.026597 | 1.324397 | -0.060004 | -0.869144 | 1.465231 | -0.902156 | 0.598522 | -1.044263 |
| 7 | 0.110500 | 0.178211 | -1.394026 | 0.897710 | -1.243991 | -0.384906 | -0.976009 | -1.391712 | 1.465231 | -1.735560 | -0.662454 | -1.567335 |
| 8 | 0.595793 | -0.898440 | 1.924099 | 0.051119 | 0.528166 | 0.377574 | -0.881206 | 1.992593 | -0.169332 | 0.289379 | -1.264549 | -0.986278 |
| 9 | -0.017542 | -1.386211 | 0.574605 | -1.268664 | -0.911663 | -1.241512 | -1.036865 | 1.992592 | -1.595580 | 0.436331 | -1.528923 | -1.224807 |
| 10 | 0.796579 | -1.216664 | -0.112327 | -0.690935 | 1.077368 | 0.874900 | -0.528379 | 1.992593 | -0.347105 | 1.295264 | 0.069115 | -0.797501 |
| 11 | 1.761253 | -0.949116 | -0.297777 | -0.913826 | -0.875567 | -0.968315 | -0.407542 | -0.016419 | -0.556271 | 0.391283 | 0.743737 | 0.460341 |
| 12 | 1.055147 | -0.394702 | 1.258031 | -0.517345 | -0.021328 | -0.557321 | 0.669125 | 1.243976 | 0.262937 | 0.337908 | 0.485227 | 2.007205 |
| 13 | 1.761253 | -0.435392 | 0.592085 | -0.692391 | 0.535758 | -0.708164 | -0.382176 | 0.125232 | -0.083947 | 0.888176 | 0.994199 | 1.087612 |
| 14 | -0.137413 | -0.980041 | -1.297302 | -0.880795 | 1.395884 | -0.901503 | -0.756382 | -0.304071 | -1.277311 | 1.299875 | -1.162641 | -1.274290 |
| 15 | 0.017749 | -0.936126 | -1.136240 | -0.862914 | 0.782285 | -0.230220 | -0.734923 | -0.295573 | -1.221528 | 1.299875 | -1.058979 | -1.237947 |
| 16 | -0.382429 | -1.386211 | -1.437693 | -1.268664 | 0.481905 | -1.219324 | -1.036865 | 0.284908 | -1.749408 | 1.299875 | -1.532659 | -1.596787 |
| 17 | -1.227998 | 1.294887 | 2.200026 | -0.318659 | 2.364886 | -1.078770 | -0.029818 | -1.427905 | -1.667310 | 0.632080 | -1.532496 | 0.393871 |
| 18 | -1.076569 | -0.776099 | -0.576736 | -0.929915 | 0.188222 | -1.067980 | 0.156056 | -0.671783 | -0.806376 | 1.299875 | -0.994450 | -0.887100 |
| 19 | -1.180746 | 0.385053 | 2.200026 | -0.005979 | 1.302093 | -0.928084 | -0.007355 | -0.793418 | -1.747571 | -0.917240 | -1.528514 | -0.042363 |
| 20 | 1.192498 | -0.224351 | -0.206238 | 0.217971 | 0.304072 | 0.949289 | -0.514273 | 1.091683 | 1.465231 | -0.718685 | -0.298094 | -0.264379 |
| 21 | 1.213472 | 0.048184 | -0.600330 | 0.007879 | 0.177356 | 1.215438 | -0.394095 | 0.833193 | 1.465231 | -0.381808 | -0.415137 | -0.479222 |
| 22 | -0.004572 | -0.052026 | 2.200026 | -0.008377 | 1.967963 | -0.419263 | -0.302302 | 0.046448 | -0.343181 | 1.114245 | -0.245566 | 0.544195 |
| 23 | -0.229893 | 0.630662 | 0.484895 | 0.048344 | -0.498767 | 0.662733 | -0.509073 | 0.013777 | 1.465231 | 0.438954 | -0.681106 | -0.745425 |
| 24 | 0.217943 | -0.177066 | 0.659322 | -0.134414 | 1.650468 | -0.583176 | -0.559211 | -0.406584 | -0.598204 | 1.299875 | -0.406852 | 0.234437 |
| 25 | -0.333597 | 0.972324 | -0.721724 | -0.554449 | -0.493410 | 0.518707 | 0.321268 | -1.070955 | -0.564510 | -0.833869 | 1.695138 | -0.783758 |
| 26 | -0.602430 | -0.028610 | -0.826715 | -0.377974 | -0.696055 | -0.182813 | -0.120202 | -0.886178 | -0.584506 | -0.673011 | 1.695138 | -0.599349 |
| 27 | -0.251812 | -0.171571 | -0.358821 | -0.244636 | -0.013843 | 0.945000 | 1.038022 | -0.727984 | 0.773167 | -0.048067 | 1.695138 | 0.563056 |
| 28 | -0.368678 | 2.234146 | -0.330756 | -0.138877 | -0.559553 | -0.851160 | -0.744847 | -0.884446 | -0.828451 | -1.011890 | -0.568588 | 0.130393 |
| 29 | -0.578621 | 2.234146 | -0.562376 | -0.105376 | -0.523903 | -0.825786 | -0.690314 | -0.809436 | -0.870132 | -1.142574 | -0.536734 | 0.859716 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -0.186741 | -1.165084 | -0.921181 | 1.397816 | -0.045251 | 1.304725 | -0.153535 | 1.038843 | 1.465231 | 0.175798 | 1.209572 | -0.558994 |
| 190 | -0.163966 | 0.884601 | 1.657605 | -0.260803 | 1.379747 | -0.732537 | -0.007962 | 1.276498 | -0.419568 | 1.299875 | -0.588765 | 1.807669 |
| 191 | -0.254097 | -0.617571 | 0.535718 | -0.297186 | 1.865594 | -0.310035 | 0.916200 | 1.349280 | -0.168599 | 1.299875 | -0.479982 | 1.556484 |
| 192 | -1.359431 | 0.228998 | 0.259367 | -1.169606 | 0.516753 | -1.080823 | 1.794240 | -0.257346 | -1.633246 | 0.226711 | -1.071882 | 2.007205 |
| 193 | 1.622617 | 2.234146 | -1.127605 | -0.273622 | -1.141311 | 0.831169 | -0.681956 | -1.023930 | 0.097381 | -1.505036 | -0.432936 | -0.703132 |
| 194 | -0.467818 | -0.314482 | 0.014154 | 0.025208 | -0.807816 | -0.093075 | 3.055878 | 0.013994 | -0.514838 | -0.698969 | 0.430445 | -0.302544 |
| 195 | -1.423661 | -0.599038 | -0.707969 | -1.084898 | -1.206212 | -0.474886 | 1.273960 | -1.605019 | -1.163780 | -1.777082 | 1.695138 | -1.000177 |
| 196 | 1.761253 | -0.325612 | 0.357954 | -0.709817 | 1.043699 | -0.927401 | -0.653399 | -0.107407 | -0.553233 | -0.078311 | -0.687092 | -0.080809 |
| 197 | 1.761253 | 1.028574 | 1.374947 | 0.116683 | 1.136600 | -0.187955 | -0.195137 | 0.341797 | -0.856237 | 0.366485 | -0.658035 | 0.645642 |
| 198 | 1.761253 | 0.050043 | 0.687757 | -0.434894 | 0.705291 | -0.680095 | -0.549325 | 0.816661 | -0.567882 | 0.584770 | -0.302600 | -0.144573 |
| 199 | -0.919189 | -0.200271 | -1.032880 | -0.020720 | -1.069924 | -0.649964 | -0.843737 | -0.600356 | 1.465231 | -0.271885 | 0.807204 | -0.901281 |
| 200 | -0.988568 | -0.189392 | -1.028379 | 0.102199 | -1.085401 | -0.667213 | -0.580786 | -0.498427 | 1.465231 | -0.287173 | 0.737684 | -0.938325 |
| 201 | -0.779270 | 0.427427 | -0.948100 | -0.165441 | -1.033638 | 0.272258 | -0.022274 | -0.198353 | 1.465231 | -0.072695 | 1.561935 | -0.848980 |
| 202 | 1.116127 | -1.139042 | 0.168460 | -1.122851 | -0.854043 | 1.067951 | -0.735589 | 0.656822 | 0.343006 | 1.299875 | 0.776152 | -0.247153 |
| 203 | 0.762440 | -1.190866 | -0.050517 | -1.116856 | -1.095215 | 0.632777 | -0.797158 | 1.167398 | 1.149430 | 1.299875 | 1.392402 | 0.047424 |
| 204 | 0.058411 | -1.128496 | -1.328850 | -1.229911 | -1.074617 | -0.085788 | -0.812628 | 0.326031 | 0.625580 | 1.299875 | 1.290873 | -0.077297 |
| 205 | -0.720270 | -0.697672 | -0.720394 | 1.813110 | -0.859595 | -0.861783 | -0.908744 | 1.216314 | 1.411592 | -0.405169 | 1.695138 | -0.439681 |
| 206 | -0.364461 | -0.010160 | -1.095500 | 0.914425 | -1.040229 | 0.885108 | -0.998962 | 0.772555 | 1.465231 | -0.838613 | 0.940082 | -0.955178 |
| 207 | -0.272236 | -0.196860 | -0.727982 | 0.141418 | -0.904008 | -0.065613 | -0.944991 | 0.795648 | 0.611803 | 0.903563 | 1.695137 | -0.158991 |
| 208 | 1.761253 | -0.082523 | -0.122308 | 0.265346 | -0.485445 | 0.500219 | 0.126422 | -0.354469 | 0.139245 | -0.560750 | 1.149004 | 0.354466 |
| 209 | 1.573516 | -0.161473 | -0.359909 | 1.241760 | -0.664508 | 1.002463 | -0.076931 | 0.519366 | 1.465231 | -0.646843 | 0.532185 | -0.215564 |
| 210 | 1.761253 | 0.417875 | -0.918851 | -0.929813 | -0.982357 | -0.771036 | -0.949451 | -0.679224 | -0.652751 | -1.604058 | -0.786724 | -0.848956 |
| 211 | 1.761253 | 0.145550 | 1.710575 | 1.914520 | 1.233461 | 2.474950 | 0.953725 | 1.263177 | 0.830563 | 0.011243 | 0.602765 | -0.024981 |
| 212 | 0.470346 | -0.334996 | 2.200026 | 1.858653 | 0.847856 | 1.364055 | 0.061293 | 1.366431 | -0.301856 | -0.501569 | -0.235203 | -0.695720 |
| 213 | -0.576457 | -0.914445 | 1.070087 | 0.337357 | 0.306857 | 0.394672 | -0.372356 | -0.450091 | -0.240434 | 0.165141 | 1.695138 | 0.345150 |
| 214 | 1.761253 | -0.591066 | -0.690824 | 2.065965 | -0.721336 | -0.340791 | -0.483151 | 0.855908 | 0.529996 | -1.116013 | 0.527710 | -0.050391 |
| 215 | 1.490806 | -1.368871 | -1.151960 | 2.846487 | -0.924825 | 0.052478 | -0.970103 | 1.992593 | 0.913611 | -0.452243 | 0.613814 | -1.529552 |
| 216 | 0.191801 | -1.348512 | -1.315236 | -0.455163 | -1.244101 | -1.240530 | -1.036865 | -0.531344 | 1.465231 | -1.809822 | -1.350349 | -1.513618 |
| 217 | -0.002098 | 2.039653 | -0.752917 | 0.971355 | -0.795869 | 0.431147 | -0.753214 | 0.043687 | 1.465231 | -1.105865 | -0.938582 | -0.984328 |
| 218 | -0.098688 | -0.923087 | -0.917548 | 0.312310 | -0.183969 | 0.248120 | -0.545773 | 0.584070 | 0.733937 | -0.697562 | 1.695138 | -0.497086 |
219 rows × 12 columns
In [519]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[519]:
[2628.0, 2183.3505259693475, 1946.7709026205612, 1764.2381744586387, 1667.52081260375, 1588.695626080069, 1521.6546082793252, 1465.3583612489235, 1381.9950671234758, 1338.0035802619554, 1275.054416118868, 1244.4334212962274, 1201.2393010186188, 1166.7934845697623]
In [520]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[520]:
[<matplotlib.lines.Line2D at 0x1b82f66a198>]
K=2
In [521]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[521]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [522]:
kmeans_ch.labels_
Out[522]:
array([0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
In [523]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[523]:
array([0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
In [524]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [525]:
X
Out[525]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.752761 | -1.114598 | -0.348132 | 2.966304 | -0.925235 | 0.552064 | -0.710296 | 0.412009 | -0.784684 | -1.667162 | -0.834151 | -1.566379 | 0 | 0 |
| 1 | -1.387006 | -0.333039 | 0.041297 | -0.917052 | 0.656635 | -1.022407 | -0.805166 | -0.905135 | -0.708805 | 1.299875 | -0.948816 | -1.413196 | 1 | 0 |
| 2 | -0.628834 | 2.234144 | 0.613536 | -0.978644 | 0.836157 | -0.735689 | -0.059767 | -1.571350 | 1.278264 | -1.103616 | -1.153426 | 0.685062 | 1 | 0 |
| 3 | 0.081693 | 1.765530 | -0.365668 | 0.759057 | -1.136519 | -0.071939 | -0.412587 | -1.310708 | 1.465231 | -1.266573 | 0.040076 | -0.308065 | 0 | 0 |
| 4 | 0.056206 | 1.646501 | 0.508800 | 0.525847 | 0.506842 | -0.390517 | -0.241209 | -0.409725 | 1.465231 | -0.148455 | -0.354779 | 0.188297 | 0 | 0 |
| 5 | -0.475284 | 0.673200 | -1.077774 | 0.360339 | -0.032017 | 0.910768 | 1.405699 | -0.748908 | 1.465231 | -0.933838 | -0.225835 | -0.922170 | 0 | 0 |
| 6 | 0.163433 | -0.011304 | -1.057752 | 1.128932 | 0.026597 | 1.324397 | -0.060004 | -0.869144 | 1.465231 | -0.902156 | 0.598522 | -1.044263 | 0 | 0 |
| 7 | 0.110500 | 0.178211 | -1.394026 | 0.897710 | -1.243991 | -0.384906 | -0.976009 | -1.391712 | 1.465231 | -1.735560 | -0.662454 | -1.567335 | 0 | 0 |
| 8 | 0.595793 | -0.898440 | 1.924099 | 0.051119 | 0.528166 | 0.377574 | -0.881206 | 1.992593 | -0.169332 | 0.289379 | -1.264549 | -0.986278 | 1 | 0 |
| 9 | -0.017542 | -1.386211 | 0.574605 | -1.268664 | -0.911663 | -1.241512 | -1.036865 | 1.992592 | -1.595580 | 0.436331 | -1.528923 | -1.224807 | 1 | 0 |
| 10 | 0.796579 | -1.216664 | -0.112327 | -0.690935 | 1.077368 | 0.874900 | -0.528379 | 1.992593 | -0.347105 | 1.295264 | 0.069115 | -0.797501 | 1 | 0 |
| 11 | 1.761253 | -0.949116 | -0.297777 | -0.913826 | -0.875567 | -0.968315 | -0.407542 | -0.016419 | -0.556271 | 0.391283 | 0.743737 | 0.460341 | 1 | 0 |
| 12 | 1.055147 | -0.394702 | 1.258031 | -0.517345 | -0.021328 | -0.557321 | 0.669125 | 1.243976 | 0.262937 | 0.337908 | 0.485227 | 2.007205 | 1 | 0 |
| 13 | 1.761253 | -0.435392 | 0.592085 | -0.692391 | 0.535758 | -0.708164 | -0.382176 | 0.125232 | -0.083947 | 0.888176 | 0.994199 | 1.087612 | 1 | 0 |
| 14 | -0.137413 | -0.980041 | -1.297302 | -0.880795 | 1.395884 | -0.901503 | -0.756382 | -0.304071 | -1.277311 | 1.299875 | -1.162641 | -1.274290 | 1 | 0 |
| 15 | 0.017749 | -0.936126 | -1.136240 | -0.862914 | 0.782285 | -0.230220 | -0.734923 | -0.295573 | -1.221528 | 1.299875 | -1.058979 | -1.237947 | 1 | 0 |
| 16 | -0.382429 | -1.386211 | -1.437693 | -1.268664 | 0.481905 | -1.219324 | -1.036865 | 0.284908 | -1.749408 | 1.299875 | -1.532659 | -1.596787 | 1 | 0 |
| 17 | -1.227998 | 1.294887 | 2.200026 | -0.318659 | 2.364886 | -1.078770 | -0.029818 | -1.427905 | -1.667310 | 0.632080 | -1.532496 | 0.393871 | 1 | 0 |
| 18 | -1.076569 | -0.776099 | -0.576736 | -0.929915 | 0.188222 | -1.067980 | 0.156056 | -0.671783 | -0.806376 | 1.299875 | -0.994450 | -0.887100 | 1 | 0 |
| 19 | -1.180746 | 0.385053 | 2.200026 | -0.005979 | 1.302093 | -0.928084 | -0.007355 | -0.793418 | -1.747571 | -0.917240 | -1.528514 | -0.042363 | 1 | 0 |
| 20 | 1.192498 | -0.224351 | -0.206238 | 0.217971 | 0.304072 | 0.949289 | -0.514273 | 1.091683 | 1.465231 | -0.718685 | -0.298094 | -0.264379 | 0 | 0 |
| 21 | 1.213472 | 0.048184 | -0.600330 | 0.007879 | 0.177356 | 1.215438 | -0.394095 | 0.833193 | 1.465231 | -0.381808 | -0.415137 | -0.479222 | 0 | 0 |
| 22 | -0.004572 | -0.052026 | 2.200026 | -0.008377 | 1.967963 | -0.419263 | -0.302302 | 0.046448 | -0.343181 | 1.114245 | -0.245566 | 0.544195 | 1 | 0 |
| 23 | -0.229893 | 0.630662 | 0.484895 | 0.048344 | -0.498767 | 0.662733 | -0.509073 | 0.013777 | 1.465231 | 0.438954 | -0.681106 | -0.745425 | 0 | 0 |
| 24 | 0.217943 | -0.177066 | 0.659322 | -0.134414 | 1.650468 | -0.583176 | -0.559211 | -0.406584 | -0.598204 | 1.299875 | -0.406852 | 0.234437 | 1 | 0 |
| 25 | -0.333597 | 0.972324 | -0.721724 | -0.554449 | -0.493410 | 0.518707 | 0.321268 | -1.070955 | -0.564510 | -0.833869 | 1.695138 | -0.783758 | 0 | 0 |
| 26 | -0.602430 | -0.028610 | -0.826715 | -0.377974 | -0.696055 | -0.182813 | -0.120202 | -0.886178 | -0.584506 | -0.673011 | 1.695138 | -0.599349 | 0 | 0 |
| 27 | -0.251812 | -0.171571 | -0.358821 | -0.244636 | -0.013843 | 0.945000 | 1.038022 | -0.727984 | 0.773167 | -0.048067 | 1.695138 | 0.563056 | 0 | 0 |
| 28 | -0.368678 | 2.234146 | -0.330756 | -0.138877 | -0.559553 | -0.851160 | -0.744847 | -0.884446 | -0.828451 | -1.011890 | -0.568588 | 0.130393 | 0 | 0 |
| 29 | -0.578621 | 2.234146 | -0.562376 | -0.105376 | -0.523903 | -0.825786 | -0.690314 | -0.809436 | -0.870132 | -1.142574 | -0.536734 | 0.859716 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -0.186741 | -1.165084 | -0.921181 | 1.397816 | -0.045251 | 1.304725 | -0.153535 | 1.038843 | 1.465231 | 0.175798 | 1.209572 | -0.558994 | 0 | 1 |
| 190 | -0.163966 | 0.884601 | 1.657605 | -0.260803 | 1.379747 | -0.732537 | -0.007962 | 1.276498 | -0.419568 | 1.299875 | -0.588765 | 1.807669 | 1 | 1 |
| 191 | -0.254097 | -0.617571 | 0.535718 | -0.297186 | 1.865594 | -0.310035 | 0.916200 | 1.349280 | -0.168599 | 1.299875 | -0.479982 | 1.556484 | 1 | 1 |
| 192 | -1.359431 | 0.228998 | 0.259367 | -1.169606 | 0.516753 | -1.080823 | 1.794240 | -0.257346 | -1.633246 | 0.226711 | -1.071882 | 2.007205 | 1 | 1 |
| 193 | 1.622617 | 2.234146 | -1.127605 | -0.273622 | -1.141311 | 0.831169 | -0.681956 | -1.023930 | 0.097381 | -1.505036 | -0.432936 | -0.703132 | 0 | 1 |
| 194 | -0.467818 | -0.314482 | 0.014154 | 0.025208 | -0.807816 | -0.093075 | 3.055878 | 0.013994 | -0.514838 | -0.698969 | 0.430445 | -0.302544 | 0 | 1 |
| 195 | -1.423661 | -0.599038 | -0.707969 | -1.084898 | -1.206212 | -0.474886 | 1.273960 | -1.605019 | -1.163780 | -1.777082 | 1.695138 | -1.000177 | 0 | 1 |
| 196 | 1.761253 | -0.325612 | 0.357954 | -0.709817 | 1.043699 | -0.927401 | -0.653399 | -0.107407 | -0.553233 | -0.078311 | -0.687092 | -0.080809 | 1 | 1 |
| 197 | 1.761253 | 1.028574 | 1.374947 | 0.116683 | 1.136600 | -0.187955 | -0.195137 | 0.341797 | -0.856237 | 0.366485 | -0.658035 | 0.645642 | 1 | 1 |
| 198 | 1.761253 | 0.050043 | 0.687757 | -0.434894 | 0.705291 | -0.680095 | -0.549325 | 0.816661 | -0.567882 | 0.584770 | -0.302600 | -0.144573 | 1 | 1 |
| 199 | -0.919189 | -0.200271 | -1.032880 | -0.020720 | -1.069924 | -0.649964 | -0.843737 | -0.600356 | 1.465231 | -0.271885 | 0.807204 | -0.901281 | 0 | 1 |
| 200 | -0.988568 | -0.189392 | -1.028379 | 0.102199 | -1.085401 | -0.667213 | -0.580786 | -0.498427 | 1.465231 | -0.287173 | 0.737684 | -0.938325 | 0 | 1 |
| 201 | -0.779270 | 0.427427 | -0.948100 | -0.165441 | -1.033638 | 0.272258 | -0.022274 | -0.198353 | 1.465231 | -0.072695 | 1.561935 | -0.848980 | 0 | 1 |
| 202 | 1.116127 | -1.139042 | 0.168460 | -1.122851 | -0.854043 | 1.067951 | -0.735589 | 0.656822 | 0.343006 | 1.299875 | 0.776152 | -0.247153 | 0 | 1 |
| 203 | 0.762440 | -1.190866 | -0.050517 | -1.116856 | -1.095215 | 0.632777 | -0.797158 | 1.167398 | 1.149430 | 1.299875 | 1.392402 | 0.047424 | 0 | 1 |
| 204 | 0.058411 | -1.128496 | -1.328850 | -1.229911 | -1.074617 | -0.085788 | -0.812628 | 0.326031 | 0.625580 | 1.299875 | 1.290873 | -0.077297 | 0 | 1 |
| 205 | -0.720270 | -0.697672 | -0.720394 | 1.813110 | -0.859595 | -0.861783 | -0.908744 | 1.216314 | 1.411592 | -0.405169 | 1.695138 | -0.439681 | 0 | 1 |
| 206 | -0.364461 | -0.010160 | -1.095500 | 0.914425 | -1.040229 | 0.885108 | -0.998962 | 0.772555 | 1.465231 | -0.838613 | 0.940082 | -0.955178 | 0 | 1 |
| 207 | -0.272236 | -0.196860 | -0.727982 | 0.141418 | -0.904008 | -0.065613 | -0.944991 | 0.795648 | 0.611803 | 0.903563 | 1.695137 | -0.158991 | 0 | 1 |
| 208 | 1.761253 | -0.082523 | -0.122308 | 0.265346 | -0.485445 | 0.500219 | 0.126422 | -0.354469 | 0.139245 | -0.560750 | 1.149004 | 0.354466 | 0 | 1 |
| 209 | 1.573516 | -0.161473 | -0.359909 | 1.241760 | -0.664508 | 1.002463 | -0.076931 | 0.519366 | 1.465231 | -0.646843 | 0.532185 | -0.215564 | 0 | 1 |
| 210 | 1.761253 | 0.417875 | -0.918851 | -0.929813 | -0.982357 | -0.771036 | -0.949451 | -0.679224 | -0.652751 | -1.604058 | -0.786724 | -0.848956 | 0 | 1 |
| 211 | 1.761253 | 0.145550 | 1.710575 | 1.914520 | 1.233461 | 2.474950 | 0.953725 | 1.263177 | 0.830563 | 0.011243 | 0.602765 | -0.024981 | 0 | 1 |
| 212 | 0.470346 | -0.334996 | 2.200026 | 1.858653 | 0.847856 | 1.364055 | 0.061293 | 1.366431 | -0.301856 | -0.501569 | -0.235203 | -0.695720 | 0 | 1 |
| 213 | -0.576457 | -0.914445 | 1.070087 | 0.337357 | 0.306857 | 0.394672 | -0.372356 | -0.450091 | -0.240434 | 0.165141 | 1.695138 | 0.345150 | 0 | 1 |
| 214 | 1.761253 | -0.591066 | -0.690824 | 2.065965 | -0.721336 | -0.340791 | -0.483151 | 0.855908 | 0.529996 | -1.116013 | 0.527710 | -0.050391 | 0 | 1 |
| 215 | 1.490806 | -1.368871 | -1.151960 | 2.846487 | -0.924825 | 0.052478 | -0.970103 | 1.992593 | 0.913611 | -0.452243 | 0.613814 | -1.529552 | 0 | 1 |
| 216 | 0.191801 | -1.348512 | -1.315236 | -0.455163 | -1.244101 | -1.240530 | -1.036865 | -0.531344 | 1.465231 | -1.809822 | -1.350349 | -1.513618 | 0 | 1 |
| 217 | -0.002098 | 2.039653 | -0.752917 | 0.971355 | -0.795869 | 0.431147 | -0.753214 | 0.043687 | 1.465231 | -1.105865 | -0.938582 | -0.984328 | 0 | 1 |
| 218 | -0.098688 | -0.923087 | -0.917548 | 0.312310 | -0.183969 | 0.248120 | -0.545773 | 0.584070 | 0.733937 | -0.697562 | 1.695138 | -0.497086 | 0 | 1 |
219 rows × 14 columns
In [526]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[526]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b829dddc50>
In [527]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[5]))
Urban Place¶
ANN¶
In [344]:
X = df_n_ps_std_ch[5]
In [345]:
y = df_n_ps[5]['chosen']
In [346]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [347]:
X_train.shape
Out[347]:
(162, 12)
In [348]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [349]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [350]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [535]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (10, 10, 10), 'learning_rate_init': 0.006, 'max_iter': 200}, que permiten obtener un Accuracy de 79.63% y un Kappa del 53.46
Tiempo total: 24.75 minutos
In [351]:
grid.best_params_={'activation': 'relu', 'hidden_layer_sizes': (10, 10, 10), 'learning_rate_init': 0.006, 'max_iter': 200}
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [352]:
input_tensor = Input(shape = (n0,))
In [353]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [354]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [355]:
model.summary()
Model: "model_19" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_21 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_55 (Dense) (None, 10) 130 _________________________________________________________________ dense_56 (Dense) (None, 10) 110 _________________________________________________________________ dense_57 (Dense) (None, 10) 110 _________________________________________________________________ dense_58 (Dense) (None, 1) 11 ================================================================= Total params: 361 Trainable params: 361 Non-trainable params: 0 _________________________________________________________________
In [356]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 162 samples, validate on 54 samples Epoch 1/200 162/162 [==============================] - 0s 2ms/step - loss: 0.7995 - accuracy: 0.3272 - val_loss: 0.7472 - val_accuracy: 0.3704 Epoch 2/200 162/162 [==============================] - 0s 86us/step - loss: 0.7034 - accuracy: 0.4815 - val_loss: 0.6900 - val_accuracy: 0.5741 Epoch 3/200 162/162 [==============================] - 0s 93us/step - loss: 0.6763 - accuracy: 0.6728 - val_loss: 0.6687 - val_accuracy: 0.6296 Epoch 4/200 162/162 [==============================] - 0s 86us/step - loss: 0.6577 - accuracy: 0.7099 - val_loss: 0.6443 - val_accuracy: 0.6296 Epoch 5/200 162/162 [==============================] - 0s 86us/step - loss: 0.6404 - accuracy: 0.6914 - val_loss: 0.6290 - val_accuracy: 0.6667 Epoch 6/200 162/162 [==============================] - 0s 93us/step - loss: 0.6270 - accuracy: 0.7037 - val_loss: 0.6180 - val_accuracy: 0.6667 Epoch 7/200 162/162 [==============================] - 0s 86us/step - loss: 0.6115 - accuracy: 0.7037 - val_loss: 0.6109 - val_accuracy: 0.6667 Epoch 8/200 162/162 [==============================] - 0s 86us/step - loss: 0.5949 - accuracy: 0.7099 - val_loss: 0.5968 - val_accuracy: 0.7037 Epoch 9/200 162/162 [==============================] - 0s 99us/step - loss: 0.5784 - accuracy: 0.7037 - val_loss: 0.5870 - val_accuracy: 0.7037 Epoch 10/200 162/162 [==============================] - 0s 111us/step - loss: 0.5631 - accuracy: 0.7099 - val_loss: 0.5827 - val_accuracy: 0.7037 Epoch 11/200 162/162 [==============================] - 0s 93us/step - loss: 0.5504 - accuracy: 0.7099 - val_loss: 0.5718 - val_accuracy: 0.6852 Epoch 12/200 162/162 [==============================] - 0s 93us/step - loss: 0.5415 - accuracy: 0.7222 - val_loss: 0.5613 - val_accuracy: 0.6852 Epoch 13/200 162/162 [==============================] - 0s 86us/step - loss: 0.5291 - accuracy: 0.7284 - val_loss: 0.5476 - val_accuracy: 0.6852 Epoch 14/200 162/162 [==============================] - 0s 86us/step - loss: 0.5194 - accuracy: 0.7160 - val_loss: 0.5379 - val_accuracy: 0.7037 Epoch 15/200 162/162 [==============================] - 0s 80us/step - loss: 0.5190 - accuracy: 0.7346 - val_loss: 0.5277 - val_accuracy: 0.7222 Epoch 16/200 162/162 [==============================] - 0s 86us/step - loss: 0.5023 - accuracy: 0.7469 - val_loss: 0.5318 - val_accuracy: 0.7778 Epoch 17/200 162/162 [==============================] - 0s 86us/step - loss: 0.4821 - accuracy: 0.7840 - val_loss: 0.5357 - val_accuracy: 0.7963 Epoch 18/200 162/162 [==============================] - 0s 86us/step - loss: 0.4650 - accuracy: 0.7840 - val_loss: 0.5308 - val_accuracy: 0.7963 Epoch 19/200 162/162 [==============================] - 0s 86us/step - loss: 0.4609 - accuracy: 0.7901 - val_loss: 0.5470 - val_accuracy: 0.7222 Epoch 20/200 162/162 [==============================] - 0s 80us/step - loss: 0.4549 - accuracy: 0.7901 - val_loss: 0.5606 - val_accuracy: 0.7222 Epoch 21/200 162/162 [==============================] - 0s 93us/step - loss: 0.4465 - accuracy: 0.8025 - val_loss: 0.5559 - val_accuracy: 0.7222 Epoch 22/200 162/162 [==============================] - 0s 74us/step - loss: 0.4472 - accuracy: 0.7840 - val_loss: 0.5403 - val_accuracy: 0.7407 Epoch 23/200 162/162 [==============================] - 0s 74us/step - loss: 0.4687 - accuracy: 0.7654 - val_loss: 0.5305 - val_accuracy: 0.7778 Epoch 24/200 162/162 [==============================] - 0s 123us/step - loss: 0.4412 - accuracy: 0.7593 - val_loss: 0.5300 - val_accuracy: 0.7963 Epoch 25/200 162/162 [==============================] - 0s 105us/step - loss: 0.4162 - accuracy: 0.7901 - val_loss: 0.5529 - val_accuracy: 0.7222 Epoch 26/200 162/162 [==============================] - 0s 99us/step - loss: 0.4090 - accuracy: 0.8210 - val_loss: 0.5396 - val_accuracy: 0.7593 Epoch 27/200 162/162 [==============================] - 0s 86us/step - loss: 0.3954 - accuracy: 0.8333 - val_loss: 0.5259 - val_accuracy: 0.7407 Epoch 00027: ReduceLROnPlateau reducing learning rate to 0.003000000026077032. Epoch 28/200 162/162 [==============================] - 0s 86us/step - loss: 0.3960 - accuracy: 0.8272 - val_loss: 0.5284 - val_accuracy: 0.7222 Epoch 29/200 162/162 [==============================] - 0s 93us/step - loss: 0.3958 - accuracy: 0.8272 - val_loss: 0.5243 - val_accuracy: 0.7593 Epoch 30/200 162/162 [==============================] - 0s 80us/step - loss: 0.3860 - accuracy: 0.8210 - val_loss: 0.5192 - val_accuracy: 0.7778 Epoch 31/200 162/162 [==============================] - 0s 99us/step - loss: 0.3801 - accuracy: 0.8210 - val_loss: 0.5134 - val_accuracy: 0.7778 Epoch 32/200 162/162 [==============================] - 0s 111us/step - loss: 0.3740 - accuracy: 0.8272 - val_loss: 0.5115 - val_accuracy: 0.7778 Epoch 33/200 162/162 [==============================] - 0s 123us/step - loss: 0.3666 - accuracy: 0.8333 - val_loss: 0.5086 - val_accuracy: 0.7963 Epoch 34/200 162/162 [==============================] - 0s 111us/step - loss: 0.3621 - accuracy: 0.8272 - val_loss: 0.5083 - val_accuracy: 0.7963 Epoch 35/200 162/162 [==============================] - 0s 105us/step - loss: 0.3571 - accuracy: 0.8272 - val_loss: 0.5057 - val_accuracy: 0.7963 Epoch 36/200 162/162 [==============================] - 0s 111us/step - loss: 0.3521 - accuracy: 0.8333 - val_loss: 0.5058 - val_accuracy: 0.7778 Epoch 37/200 162/162 [==============================] - 0s 111us/step - loss: 0.3466 - accuracy: 0.8333 - val_loss: 0.5061 - val_accuracy: 0.7963 Epoch 00037: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 38/200 162/162 [==============================] - 0s 105us/step - loss: 0.3428 - accuracy: 0.8395 - val_loss: 0.5041 - val_accuracy: 0.7963 Epoch 39/200 162/162 [==============================] - 0s 111us/step - loss: 0.3407 - accuracy: 0.8457 - val_loss: 0.5080 - val_accuracy: 0.7778 Epoch 40/200 162/162 [==============================] - 0s 111us/step - loss: 0.3380 - accuracy: 0.8395 - val_loss: 0.5229 - val_accuracy: 0.7778 Epoch 41/200 162/162 [==============================] - 0s 117us/step - loss: 0.3401 - accuracy: 0.8457 - val_loss: 0.5307 - val_accuracy: 0.7778 Epoch 42/200 162/162 [==============================] - 0s 105us/step - loss: 0.3417 - accuracy: 0.8395 - val_loss: 0.5363 - val_accuracy: 0.7778 Epoch 43/200 162/162 [==============================] - 0s 117us/step - loss: 0.3384 - accuracy: 0.8395 - val_loss: 0.5282 - val_accuracy: 0.7778 Epoch 44/200 162/162 [==============================] - 0s 111us/step - loss: 0.3347 - accuracy: 0.8395 - val_loss: 0.5174 - val_accuracy: 0.7778 Epoch 45/200 162/162 [==============================] - 0s 117us/step - loss: 0.3302 - accuracy: 0.8395 - val_loss: 0.5105 - val_accuracy: 0.7778 Epoch 46/200 162/162 [==============================] - 0s 117us/step - loss: 0.3280 - accuracy: 0.8457 - val_loss: 0.5056 - val_accuracy: 0.7778 Epoch 47/200 162/162 [==============================] - ETA: 0s - loss: 0.2933 - accuracy: 0.87 - 0s 142us/step - loss: 0.3258 - accuracy: 0.8519 - val_loss: 0.5080 - val_accuracy: 0.7778 Epoch 00047: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 48/200 162/162 [==============================] - 0s 123us/step - loss: 0.3239 - accuracy: 0.8642 - val_loss: 0.5072 - val_accuracy: 0.7778 Epoch 49/200 162/162 [==============================] - 0s 136us/step - loss: 0.3227 - accuracy: 0.8580 - val_loss: 0.5070 - val_accuracy: 0.7778 Epoch 50/200 162/162 [==============================] - 0s 117us/step - loss: 0.3219 - accuracy: 0.8580 - val_loss: 0.5088 - val_accuracy: 0.7778 Epoch 51/200 162/162 [==============================] - 0s 130us/step - loss: 0.3200 - accuracy: 0.8642 - val_loss: 0.5089 - val_accuracy: 0.7778 Epoch 52/200 162/162 [==============================] - 0s 105us/step - loss: 0.3190 - accuracy: 0.8580 - val_loss: 0.5070 - val_accuracy: 0.7778 Epoch 53/200 162/162 [==============================] - 0s 111us/step - loss: 0.3170 - accuracy: 0.8642 - val_loss: 0.5048 - val_accuracy: 0.7778 Epoch 54/200 162/162 [==============================] - ETA: 0s - loss: 0.4040 - accuracy: 0.81 - 0s 117us/step - loss: 0.3167 - accuracy: 0.8580 - val_loss: 0.5008 - val_accuracy: 0.7778 Epoch 55/200 162/162 [==============================] - 0s 111us/step - loss: 0.3155 - accuracy: 0.8642 - val_loss: 0.4981 - val_accuracy: 0.7778 Epoch 56/200 162/162 [==============================] - 0s 111us/step - loss: 0.3149 - accuracy: 0.8642 - val_loss: 0.4947 - val_accuracy: 0.7963 Epoch 57/200 162/162 [==============================] - 0s 111us/step - loss: 0.3135 - accuracy: 0.8642 - val_loss: 0.4910 - val_accuracy: 0.7963 Epoch 00057: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 58/200 162/162 [==============================] - 0s 123us/step - loss: 0.3136 - accuracy: 0.8704 - val_loss: 0.4902 - val_accuracy: 0.7963 Epoch 59/200 162/162 [==============================] - 0s 142us/step - loss: 0.3132 - accuracy: 0.8704 - val_loss: 0.4900 - val_accuracy: 0.7963 Epoch 60/200 162/162 [==============================] - 0s 123us/step - loss: 0.3127 - accuracy: 0.8704 - val_loss: 0.4898 - val_accuracy: 0.7963 Epoch 61/200 162/162 [==============================] - 0s 111us/step - loss: 0.3121 - accuracy: 0.8704 - val_loss: 0.4903 - val_accuracy: 0.7963 Epoch 62/200 162/162 [==============================] - 0s 111us/step - loss: 0.3114 - accuracy: 0.8704 - val_loss: 0.4911 - val_accuracy: 0.7963 Epoch 63/200 162/162 [==============================] - 0s 111us/step - loss: 0.3105 - accuracy: 0.8704 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 64/200 162/162 [==============================] - 0s 123us/step - loss: 0.3088 - accuracy: 0.8704 - val_loss: 0.4946 - val_accuracy: 0.7963 Epoch 65/200 162/162 [==============================] - 0s 105us/step - loss: 0.3078 - accuracy: 0.8765 - val_loss: 0.4955 - val_accuracy: 0.7963 Epoch 66/200 162/162 [==============================] - 0s 105us/step - loss: 0.3076 - accuracy: 0.8765 - val_loss: 0.4960 - val_accuracy: 0.7963 Epoch 67/200 162/162 [==============================] - 0s 105us/step - loss: 0.3070 - accuracy: 0.8704 - val_loss: 0.4962 - val_accuracy: 0.7963 Epoch 00067: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 68/200 162/162 [==============================] - 0s 105us/step - loss: 0.3065 - accuracy: 0.8704 - val_loss: 0.4960 - val_accuracy: 0.7963 Epoch 69/200 162/162 [==============================] - 0s 117us/step - loss: 0.3063 - accuracy: 0.8704 - val_loss: 0.4959 - val_accuracy: 0.7963 Epoch 70/200 162/162 [==============================] - 0s 117us/step - loss: 0.3061 - accuracy: 0.8704 - val_loss: 0.4957 - val_accuracy: 0.7963 Epoch 71/200 162/162 [==============================] - 0s 111us/step - loss: 0.3060 - accuracy: 0.8765 - val_loss: 0.4944 - val_accuracy: 0.7963 Epoch 72/200 162/162 [==============================] - 0s 123us/step - loss: 0.3059 - accuracy: 0.8765 - val_loss: 0.4937 - val_accuracy: 0.7963 Epoch 73/200 162/162 [==============================] - 0s 111us/step - loss: 0.3058 - accuracy: 0.8827 - val_loss: 0.4936 - val_accuracy: 0.7963 Epoch 74/200 162/162 [==============================] - 0s 105us/step - loss: 0.3057 - accuracy: 0.8827 - val_loss: 0.4934 - val_accuracy: 0.7963 Epoch 75/200 162/162 [==============================] - 0s 111us/step - loss: 0.3055 - accuracy: 0.8827 - val_loss: 0.4933 - val_accuracy: 0.7963 Epoch 76/200 162/162 [==============================] - 0s 117us/step - loss: 0.3052 - accuracy: 0.8765 - val_loss: 0.4926 - val_accuracy: 0.7963 Epoch 77/200 162/162 [==============================] - 0s 111us/step - loss: 0.3053 - accuracy: 0.8765 - val_loss: 0.4912 - val_accuracy: 0.7963 Epoch 00077: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 78/200 162/162 [==============================] - 0s 111us/step - loss: 0.3051 - accuracy: 0.8765 - val_loss: 0.4909 - val_accuracy: 0.7963 Epoch 79/200 162/162 [==============================] - 0s 111us/step - loss: 0.3051 - accuracy: 0.8765 - val_loss: 0.4908 - val_accuracy: 0.7963 Epoch 80/200 162/162 [==============================] - 0s 117us/step - loss: 0.3049 - accuracy: 0.8765 - val_loss: 0.4909 - val_accuracy: 0.7963 Epoch 81/200 162/162 [==============================] - 0s 142us/step - loss: 0.3048 - accuracy: 0.8765 - val_loss: 0.4909 - val_accuracy: 0.7963 Epoch 82/200 162/162 [==============================] - 0s 142us/step - loss: 0.3046 - accuracy: 0.8765 - val_loss: 0.4910 - val_accuracy: 0.7963 Epoch 83/200 162/162 [==============================] - 0s 111us/step - loss: 0.3045 - accuracy: 0.8765 - val_loss: 0.4913 - val_accuracy: 0.7963 Epoch 84/200 162/162 [==============================] - 0s 105us/step - loss: 0.3043 - accuracy: 0.8765 - val_loss: 0.4909 - val_accuracy: 0.7963 Epoch 85/200 162/162 [==============================] - 0s 111us/step - loss: 0.3041 - accuracy: 0.8765 - val_loss: 0.4907 - val_accuracy: 0.7963 Epoch 86/200 162/162 [==============================] - 0s 105us/step - loss: 0.3040 - accuracy: 0.8765 - val_loss: 0.4908 - val_accuracy: 0.7963 Epoch 87/200 162/162 [==============================] - 0s 105us/step - loss: 0.3038 - accuracy: 0.8765 - val_loss: 0.4909 - val_accuracy: 0.7963 Epoch 00087: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 88/200 162/162 [==============================] - 0s 111us/step - loss: 0.3037 - accuracy: 0.8765 - val_loss: 0.4910 - val_accuracy: 0.7963 Epoch 89/200 162/162 [==============================] - ETA: 0s - loss: 0.2736 - accuracy: 0.93 - 0s 111us/step - loss: 0.3036 - accuracy: 0.8765 - val_loss: 0.4911 - val_accuracy: 0.7963 Epoch 90/200 162/162 [==============================] - 0s 117us/step - loss: 0.3035 - accuracy: 0.8765 - val_loss: 0.4911 - val_accuracy: 0.7963 Epoch 91/200 162/162 [==============================] - 0s 111us/step - loss: 0.3034 - accuracy: 0.8765 - val_loss: 0.4912 - val_accuracy: 0.7963 Epoch 92/200 162/162 [==============================] - 0s 136us/step - loss: 0.3033 - accuracy: 0.8765 - val_loss: 0.4913 - val_accuracy: 0.7963 Epoch 93/200 162/162 [==============================] - 0s 117us/step - loss: 0.3033 - accuracy: 0.8765 - val_loss: 0.4915 - val_accuracy: 0.7963 Epoch 94/200 162/162 [==============================] - 0s 111us/step - loss: 0.3031 - accuracy: 0.8765 - val_loss: 0.4919 - val_accuracy: 0.7963 Epoch 95/200 162/162 [==============================] - 0s 117us/step - loss: 0.3030 - accuracy: 0.8765 - val_loss: 0.4921 - val_accuracy: 0.7963 Epoch 96/200 162/162 [==============================] - 0s 123us/step - loss: 0.3030 - accuracy: 0.8827 - val_loss: 0.4923 - val_accuracy: 0.7963 Epoch 97/200 162/162 [==============================] - 0s 148us/step - loss: 0.3029 - accuracy: 0.8827 - val_loss: 0.4925 - val_accuracy: 0.7963 Epoch 00097: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 98/200 162/162 [==============================] - 0s 130us/step - loss: 0.3027 - accuracy: 0.8827 - val_loss: 0.4926 - val_accuracy: 0.7963 Epoch 99/200 162/162 [==============================] - 0s 111us/step - loss: 0.3027 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 100/200 162/162 [==============================] - 0s 111us/step - loss: 0.3027 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 101/200 162/162 [==============================] - 0s 117us/step - loss: 0.3026 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 102/200 162/162 [==============================] - 0s 111us/step - loss: 0.3026 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 103/200 162/162 [==============================] - 0s 111us/step - loss: 0.3025 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 104/200 162/162 [==============================] - 0s 117us/step - loss: 0.3025 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 105/200 162/162 [==============================] - 0s 111us/step - loss: 0.3025 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 106/200 162/162 [==============================] - 0s 99us/step - loss: 0.3024 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 107/200 162/162 [==============================] - 0s 111us/step - loss: 0.3024 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 00107: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 108/200 162/162 [==============================] - 0s 111us/step - loss: 0.3024 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 109/200 162/162 [==============================] - 0s 111us/step - loss: 0.3024 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 110/200 162/162 [==============================] - 0s 117us/step - loss: 0.3023 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 111/200 162/162 [==============================] - 0s 117us/step - loss: 0.3023 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 112/200 162/162 [==============================] - 0s 117us/step - loss: 0.3023 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 113/200 162/162 [==============================] - 0s 130us/step - loss: 0.3023 - accuracy: 0.8827 - val_loss: 0.4931 - val_accuracy: 0.7963 Epoch 114/200 162/162 [==============================] - 0s 136us/step - loss: 0.3023 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 115/200 162/162 [==============================] - 0s 105us/step - loss: 0.3023 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 116/200 162/162 [==============================] - 0s 117us/step - loss: 0.3023 - accuracy: 0.8827 - val_loss: 0.4930 - val_accuracy: 0.7963 Epoch 117/200 162/162 [==============================] - 0s 123us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 00117: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 118/200 162/162 [==============================] - 0s 148us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 119/200 162/162 [==============================] - 0s 123us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 120/200 162/162 [==============================] - 0s 117us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 121/200 162/162 [==============================] - 0s 123us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 122/200 162/162 [==============================] - 0s 123us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 123/200 162/162 [==============================] - 0s 111us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 124/200 162/162 [==============================] - 0s 111us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 125/200 162/162 [==============================] - 0s 105us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 126/200 162/162 [==============================] - 0s 117us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 127/200 162/162 [==============================] - 0s 111us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 00127: ReduceLROnPlateau reducing learning rate to 2.9296875254658516e-06. Epoch 128/200 162/162 [==============================] - ETA: 0s - loss: 0.3428 - accuracy: 0.87 - 0s 117us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 129/200 162/162 [==============================] - 0s 105us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 130/200 162/162 [==============================] - 0s 105us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 131/200 162/162 [==============================] - 0s 111us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 132/200 162/162 [==============================] - 0s 117us/step - loss: 0.3022 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 133/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 134/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 135/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 136/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 137/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 00137: ReduceLROnPlateau reducing learning rate to 1.4648437627329258e-06. Epoch 138/200 162/162 [==============================] - 0s 136us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 139/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 140/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 141/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 142/200 162/162 [==============================] - 0s 123us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 143/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4929 - val_accuracy: 0.7963 Epoch 144/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 145/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 146/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 147/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 00147: ReduceLROnPlateau reducing learning rate to 7.324218813664629e-07. Epoch 148/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 149/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 150/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 151/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 152/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 153/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 154/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 155/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 156/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 157/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 00157: ReduceLROnPlateau reducing learning rate to 3.6621094068323146e-07. Epoch 158/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 159/200 162/162 [==============================] - 0s 99us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 160/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 161/200 162/162 [==============================] - 0s 148us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 162/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 163/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 164/200 162/162 [==============================] - 0s 123us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 165/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 166/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 167/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 00167: ReduceLROnPlateau reducing learning rate to 1.8310547034161573e-07. Epoch 168/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 169/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 170/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 171/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 172/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 173/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 174/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 175/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 176/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 177/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 00177: ReduceLROnPlateau reducing learning rate to 9.155273517080786e-08. Epoch 178/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 179/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 180/200 162/162 [==============================] - ETA: 0s - loss: 0.3056 - accuracy: 0.81 - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 181/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 182/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 183/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 184/200 162/162 [==============================] - 0s 99us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 185/200 162/162 [==============================] - 0s 130us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 186/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 187/200 162/162 [==============================] - ETA: 0s - loss: 0.3155 - accuracy: 0.78 - 0s 142us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 00187: ReduceLROnPlateau reducing learning rate to 4.577636758540393e-08. Epoch 188/200 162/162 [==============================] - 0s 136us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 189/200 162/162 [==============================] - 0s 130us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 190/200 162/162 [==============================] - 0s 123us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 191/200 162/162 [==============================] - 0s 123us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 192/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 193/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 194/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 195/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 196/200 162/162 [==============================] - 0s 117us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 197/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 00197: ReduceLROnPlateau reducing learning rate to 2.2888183792701966e-08. Epoch 198/200 162/162 [==============================] - 0s 111us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 199/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963 Epoch 200/200 162/162 [==============================] - 0s 105us/step - loss: 0.3021 - accuracy: 0.8827 - val_loss: 0.4928 - val_accuracy: 0.7963
In [357]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 200)
In [358]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
54/54 [==============================] - 0s 56us/step test loss: 0.49283186263508266, test accuracy: 0.7962962985038757
In [359]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.8210526315789474
In [360]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
Kappa: 0.547945205479452 [[30 5] [ 6 13]]
KMeans¶
In [546]:
X
Out[546]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.592360 | -0.453705 | 2.211117 | -0.229901 | 0.645559 | -0.393204 | 0.424372 | -0.229283 | -0.693774 | 1.147599 | -0.220378 | 0.262481 |
| 1 | -0.441523 | 1.666549 | -0.417753 | 1.148299 | 1.047397 | -0.025553 | 0.529401 | -0.322715 | 0.788981 | -0.702420 | -0.209698 | 1.924655 |
| 2 | -0.397413 | 1.044025 | 0.141778 | 1.742806 | 0.991984 | 0.907397 | 1.476307 | 0.831252 | 1.523918 | 0.670640 | -0.046392 | 0.497913 |
| 3 | -1.385913 | -1.136470 | -1.159142 | -0.779634 | -1.287956 | -1.177352 | -1.012282 | 0.555960 | 1.314559 | 0.253537 | 1.699829 | -1.383790 |
| 4 | -0.410804 | -0.664148 | -0.936463 | -0.072578 | -1.082182 | -0.957161 | -1.022927 | -0.233777 | 0.259301 | -0.358189 | 1.699829 | -0.811341 |
| 5 | 0.654434 | -0.480395 | -0.564938 | 0.557637 | -0.695855 | -0.602536 | -0.825163 | -0.588546 | 1.523917 | -0.823244 | 0.754639 | -0.645843 |
| 6 | -0.375055 | -1.465338 | 1.649386 | -1.257929 | 2.008574 | -0.563748 | 1.506384 | 0.579723 | -1.744781 | -0.548854 | -1.508425 | -0.391068 |
| 7 | 0.064972 | -0.292924 | 1.874810 | 0.363856 | 1.341844 | 0.251944 | 1.013951 | 1.655213 | 0.115402 | 1.527592 | -0.325145 | 0.025517 |
| 8 | 0.457188 | -1.280226 | 0.508305 | 0.536904 | 2.008575 | 0.193572 | -0.498497 | 0.270656 | -1.409592 | -0.431942 | -1.096868 | -1.161518 |
| 9 | 0.264205 | -0.451954 | 0.540019 | -0.518915 | 0.759702 | 2.591054 | -0.083006 | 0.304222 | -0.558774 | 0.300192 | -0.287566 | -0.493630 |
| 10 | 1.615916 | -0.476284 | 0.254007 | -0.624451 | 0.097446 | 0.217831 | -0.040382 | 1.768809 | -0.319326 | 0.979400 | 0.016016 | -0.153245 |
| 11 | 0.834285 | -1.405123 | 0.704867 | -0.689665 | 2.008575 | 1.203152 | -1.002498 | -0.667904 | -1.688291 | -1.156923 | -0.737306 | -0.879992 |
| 12 | -0.368007 | 0.683133 | -0.474222 | 0.547694 | -0.820217 | 0.364380 | -0.474588 | -0.674021 | 1.523918 | -0.793291 | 1.078068 | -0.457193 |
| 13 | -0.839289 | 0.306633 | -0.853660 | 0.723116 | 0.435777 | -0.879183 | 0.335639 | 0.593469 | 0.708144 | -0.396821 | -0.242968 | 1.924655 |
| 14 | -0.319898 | 0.340082 | -0.491559 | 0.701985 | -0.842146 | 0.597909 | -0.644831 | -0.662251 | 1.523918 | -0.575211 | 0.534899 | -0.527162 |
| 15 | -0.609565 | 0.412613 | -0.695381 | 1.316178 | -0.719769 | 0.865430 | 1.679917 | -0.849334 | 1.523918 | -0.735830 | 0.144609 | 0.242574 |
| 16 | -0.294394 | 0.741619 | 0.151913 | 2.539991 | 0.416363 | 0.148867 | 1.677200 | 0.822220 | 1.003847 | -0.526991 | 0.785121 | -0.293878 |
| 17 | 1.116695 | -0.420423 | 1.149167 | 0.301950 | 0.500878 | 1.773601 | -0.060893 | 0.672677 | 0.289742 | 1.527593 | 1.283583 | 0.238430 |
| 18 | 0.842794 | -0.575729 | 0.643169 | -0.602296 | -0.331996 | 1.529202 | -0.867435 | 0.191979 | -0.217921 | 0.807237 | 1.699829 | 0.130123 |
| 19 | -0.030794 | 0.030288 | 0.462185 | -0.769191 | -0.543974 | 2.591054 | -0.487337 | -0.076347 | -0.991742 | 0.900076 | -0.242676 | -1.415815 |
| 20 | -1.324459 | 2.249029 | -1.202104 | -0.290955 | -0.053323 | -1.169169 | -0.449254 | -1.306597 | -0.432854 | -0.500100 | -1.455507 | 0.028032 |
| 21 | -1.391553 | 2.249029 | -1.278978 | -0.888575 | -0.811278 | -1.180328 | 0.198450 | -1.316982 | -0.927827 | -1.028770 | -1.496198 | 0.693363 |
| 22 | -0.662886 | 0.307712 | -0.805667 | 1.248429 | -0.021261 | -0.853836 | 0.505072 | -0.109644 | 0.239978 | -0.008774 | -0.437828 | 1.924655 |
| 23 | 1.105419 | -0.629615 | 0.090579 | 2.059265 | -0.742273 | 0.287915 | -0.889948 | 0.338598 | 0.769168 | 0.080426 | 1.699829 | -0.385717 |
| 24 | 1.043642 | -0.624779 | 0.129667 | 1.899266 | -0.669239 | 0.492505 | -0.813576 | 0.318395 | 0.576968 | -0.083727 | 1.699829 | -0.180141 |
| 25 | 0.301993 | -0.705708 | -0.070367 | 0.439151 | -0.783839 | 1.504692 | -0.883292 | -0.324238 | 0.394206 | -0.681481 | 1.699829 | -0.468423 |
| 26 | 1.404180 | -1.101276 | -1.004741 | 0.000759 | -1.064381 | -0.992797 | -0.971380 | 1.704361 | 0.968314 | 0.696274 | 1.699829 | -0.198305 |
| 27 | -0.634723 | -0.430657 | -0.787967 | 1.284818 | -0.780406 | -0.201832 | 0.438439 | -0.741240 | 1.523918 | -0.871410 | -0.336990 | -0.773167 |
| 28 | 0.317357 | -0.327927 | 0.301217 | -0.742175 | 0.030999 | -0.632488 | -0.114595 | 2.018795 | -0.481880 | 0.192970 | -0.259076 | 0.650325 |
| 29 | -0.648337 | -1.398422 | 0.413041 | -1.220330 | -0.442423 | -1.123348 | -0.408672 | 2.018795 | -1.250844 | -0.200036 | -1.302509 | -0.368660 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | 0.393077 | -0.250074 | 0.038107 | -0.191832 | -0.290416 | 1.007564 | -0.703416 | -0.310414 | -0.798470 | -0.796349 | 1.699829 | -0.544372 |
| 187 | -1.377643 | -0.803877 | 2.211117 | -0.707481 | 1.879500 | 0.084271 | 0.023163 | -1.061854 | -1.515997 | 0.877885 | -1.504060 | -1.062191 |
| 188 | 1.615917 | 0.379972 | 0.224721 | -0.034053 | 1.116997 | 1.461618 | -0.476350 | 1.089453 | 0.375577 | -0.678774 | 0.197515 | -0.407654 |
| 189 | 1.615917 | -0.530983 | -0.059684 | -0.924859 | 0.297570 | -0.788527 | -0.863155 | 0.507705 | -1.231423 | 0.671175 | -1.167708 | -1.068322 |
| 190 | -0.315846 | -1.045971 | 1.094836 | -1.257540 | -0.646452 | -0.759760 | -1.119000 | 1.113215 | -1.727260 | 1.527592 | -1.499848 | -1.441971 |
| 191 | -0.183414 | -1.108627 | 0.780160 | -1.125961 | -0.797291 | -0.594726 | -1.084903 | -0.937498 | -1.226658 | 1.527592 | -1.097546 | -0.723262 |
| 192 | 1.615917 | -1.079754 | 0.603712 | -1.142855 | -0.595133 | -1.113955 | -1.106489 | 0.175898 | -1.304200 | -0.527112 | -1.252788 | -0.999291 |
| 193 | 1.615917 | -0.514731 | 1.852987 | -0.738263 | -0.091935 | 0.444710 | -0.919840 | 0.338934 | -0.676013 | 1.060982 | -0.689200 | -0.040139 |
| 194 | -1.023096 | -0.007046 | -0.867797 | -0.555651 | -0.693210 | -0.998105 | 0.001088 | -0.891619 | 1.203890 | -0.871836 | -0.513026 | 1.924654 |
| 195 | 0.242106 | -0.524011 | -0.569132 | -0.770429 | 1.699110 | -0.415382 | -0.903373 | -0.100685 | -0.974586 | 1.527592 | -0.629775 | -0.696446 |
| 196 | -1.369321 | -0.969464 | 2.211117 | -1.188189 | -0.580502 | -1.060826 | 0.122236 | -0.950228 | -1.726779 | 0.105209 | -1.501989 | -0.426102 |
| 197 | -1.015340 | -0.530076 | -0.283168 | -1.082866 | -0.835974 | -1.147408 | 0.419459 | -1.191474 | -0.661554 | 1.527592 | -0.997834 | 1.722317 |
| 198 | -0.850883 | -0.441126 | 0.243450 | 0.222028 | 1.249673 | -0.347006 | -0.107132 | -0.257125 | 0.126194 | 1.527592 | -0.422391 | 1.077974 |
| 199 | 1.615917 | -0.957115 | -0.818624 | -0.751346 | 0.920566 | 0.942750 | -0.885506 | 0.827017 | -0.967859 | 0.218694 | -0.831072 | 0.035852 |
| 200 | 1.615917 | -1.094018 | -1.161937 | -0.817956 | 0.985373 | 0.188513 | -0.904777 | 0.516967 | -1.230226 | 1.160960 | -0.827322 | 0.309158 |
| 201 | 1.615917 | -1.168115 | -1.198039 | -0.967002 | 1.353491 | 1.155611 | -0.998698 | 1.025620 | -1.193487 | -0.101131 | -1.079050 | 0.318717 |
| 202 | 0.887584 | -1.008954 | 1.434477 | -0.648944 | 1.812023 | -1.119854 | 0.217275 | 1.663147 | -1.635939 | -0.375805 | -1.213970 | 1.924655 |
| 203 | -0.281732 | -0.334833 | 0.143734 | -0.498620 | 0.200931 | -0.330363 | -0.167310 | 1.001306 | -0.272888 | 0.327019 | 1.070418 | 1.924654 |
| 204 | -0.135492 | -0.180497 | 0.349882 | -0.411950 | 0.523304 | -0.164545 | -0.419377 | 1.939169 | -0.073775 | 0.044241 | 1.347349 | 1.924654 |
| 205 | 1.615917 | -0.407161 | 1.545772 | 0.566414 | 0.881235 | -0.072638 | -0.448623 | 1.725723 | -0.162715 | 1.468765 | -0.660579 | -0.516106 |
| 206 | 1.404839 | -0.218548 | 0.899766 | -0.292046 | 1.212597 | 1.247130 | -0.134708 | 2.018795 | -0.672111 | 0.036232 | -0.857434 | -0.755932 |
| 207 | 1.593455 | -0.041945 | 1.395647 | -0.303439 | 1.481579 | 0.972266 | -0.240798 | 2.018795 | -0.629725 | -0.240328 | -0.765081 | -0.553387 |
| 208 | 1.395568 | 1.540836 | -0.840747 | 0.193399 | -0.891845 | 2.198621 | -0.578460 | -0.895624 | -0.218762 | -1.304397 | 1.699829 | -0.937437 |
| 209 | 1.583225 | 1.673175 | -1.151481 | -0.093932 | -1.043532 | 2.078341 | -1.020401 | -0.782673 | -0.238115 | -1.519520 | 1.699829 | -1.128162 |
| 210 | 1.370331 | 1.252913 | -1.149111 | 0.407119 | -0.751369 | 1.134063 | -0.818484 | -1.134854 | -0.210751 | -1.317205 | 1.699829 | -1.128242 |
| 211 | 1.041007 | -1.290910 | -1.008073 | -1.187425 | -0.642020 | -0.253467 | -1.115999 | -1.223980 | -1.338703 | 1.527592 | -0.933271 | -0.918089 |
| 212 | 1.615915 | -1.334768 | -0.029491 | -1.257929 | -0.839407 | -1.180328 | -1.119000 | 0.430783 | -1.413857 | -1.452312 | -1.464581 | -1.270209 |
| 213 | -0.744293 | 0.244034 | -1.095374 | -0.843102 | -0.676799 | -1.167238 | 0.315258 | -1.196862 | -0.503734 | 0.774377 | -0.704666 | 1.924655 |
| 214 | 0.406336 | 0.410463 | -0.317774 | 0.569095 | -0.738039 | 0.037857 | -0.548951 | -0.093707 | -0.061980 | -0.002270 | 1.699829 | 1.020268 |
| 215 | -1.011813 | -0.264498 | -0.827835 | -0.483839 | 1.047147 | -0.994911 | -0.261502 | -0.762674 | 0.219646 | 0.586379 | -0.527753 | 1.924654 |
216 rows × 12 columns
In [547]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[547]:
[2592.0, 2098.7293789346745, 1732.4188990905318, 1606.2630867887576, 1500.2148167696203, 1423.6001104969187, 1355.4724990652844, 1283.649878481895, 1213.5364716691665, 1172.587492994494, 1128.5861906886926, 1087.5581685344223, 1057.5011291947897, 1021.2765793863816]
In [548]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[548]:
[<matplotlib.lines.Line2D at 0x1b83176b470>]
K=6
In [549]:
kmeans_ch = KMeans(n_clusters=6, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[549]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=6, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [550]:
kmeans_ch.labels_
Out[550]:
array([1, 3, 3, 0, 0, 0, 1, 1, 2, 2, 2, 2, 0, 3, 0, 0, 5, 5, 5, 2, 4, 4,
3, 5, 5, 5, 5, 0, 2, 2, 2, 1, 4, 1, 0, 0, 0, 5, 5, 5, 4, 4, 4, 0,
5, 0, 1, 1, 3, 3, 4, 3, 5, 5, 0, 5, 4, 4, 3, 5, 5, 5, 0, 3, 3, 3,
1, 1, 1, 5, 5, 5, 1, 3, 0, 3, 5, 4, 4, 4, 3, 3, 4, 3, 0, 3, 4, 4,
2, 2, 2, 2, 2, 4, 4, 2, 3, 0, 0, 0, 4, 5, 5, 5, 4, 3, 3, 0, 5, 5,
5, 0, 0, 4, 2, 0, 2, 2, 4, 4, 4, 2, 5, 2, 0, 0, 0, 0, 5, 3, 0, 3,
3, 1, 1, 1, 5, 2, 3, 2, 1, 1, 1, 4, 2, 0, 2, 2, 0, 0, 2, 4, 4, 2,
1, 1, 2, 0, 4, 0, 0, 5, 3, 0, 1, 0, 3, 3, 3, 5, 5, 5, 1, 3, 3, 2,
5, 0, 0, 2, 0, 1, 2, 2, 2, 0, 5, 1, 5, 2, 2, 2, 2, 2, 3, 2, 1, 3,
3, 2, 2, 2, 1, 3, 3, 2, 2, 2, 0, 0, 0, 2, 2, 3, 5, 3])
In [551]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[551]:
array([1, 3, 3, 0, 0, 0, 1, 1, 2, 2, 2, 2, 0, 3, 0, 0, 5, 5, 5, 2, 4, 4,
3, 5, 5, 5, 5, 0, 2, 2, 2, 1, 4, 1, 0, 0, 0, 5, 5, 5, 4, 4, 4, 0,
5, 0, 1, 1, 3, 3, 4, 3, 5, 5, 0, 5, 4, 4, 3, 5, 5, 5, 0, 3, 3, 3,
1, 1, 1, 5, 5, 5, 1, 3, 0, 3, 5, 4, 4, 4, 3, 3, 4, 3, 0, 3, 4, 4,
2, 2, 2, 2, 2, 4, 4, 2, 3, 0, 0, 0, 4, 5, 5, 5, 4, 3, 3, 0, 5, 5,
5, 0, 0, 4, 2, 0, 2, 2, 4, 4, 4, 2, 5, 2, 0, 0, 0, 0, 5, 3, 0, 3,
3, 1, 1, 1, 5, 2, 3, 2, 1, 1, 1, 4, 2, 0, 2, 2, 0, 0, 2, 4, 4, 2,
1, 1, 2, 0, 4, 0, 0, 5, 3, 0, 1, 0, 3, 3, 3, 5, 5, 5, 1, 3, 3, 2,
5, 0, 0, 2, 0, 1, 2, 2, 2, 0, 5, 1, 5, 2, 2, 2, 2, 2, 3, 2, 1, 3,
3, 2, 2, 2, 1, 3, 3, 2, 2, 2, 0, 0, 0, 2, 2, 3, 5, 3])
In [552]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [553]:
X
Out[553]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.592360 | -0.453705 | 2.211117 | -0.229901 | 0.645559 | -0.393204 | 0.424372 | -0.229283 | -0.693774 | 1.147599 | -0.220378 | 0.262481 | 1 | 0 |
| 1 | -0.441523 | 1.666549 | -0.417753 | 1.148299 | 1.047397 | -0.025553 | 0.529401 | -0.322715 | 0.788981 | -0.702420 | -0.209698 | 1.924655 | 3 | 0 |
| 2 | -0.397413 | 1.044025 | 0.141778 | 1.742806 | 0.991984 | 0.907397 | 1.476307 | 0.831252 | 1.523918 | 0.670640 | -0.046392 | 0.497913 | 3 | 0 |
| 3 | -1.385913 | -1.136470 | -1.159142 | -0.779634 | -1.287956 | -1.177352 | -1.012282 | 0.555960 | 1.314559 | 0.253537 | 1.699829 | -1.383790 | 0 | 0 |
| 4 | -0.410804 | -0.664148 | -0.936463 | -0.072578 | -1.082182 | -0.957161 | -1.022927 | -0.233777 | 0.259301 | -0.358189 | 1.699829 | -0.811341 | 0 | 0 |
| 5 | 0.654434 | -0.480395 | -0.564938 | 0.557637 | -0.695855 | -0.602536 | -0.825163 | -0.588546 | 1.523917 | -0.823244 | 0.754639 | -0.645843 | 0 | 0 |
| 6 | -0.375055 | -1.465338 | 1.649386 | -1.257929 | 2.008574 | -0.563748 | 1.506384 | 0.579723 | -1.744781 | -0.548854 | -1.508425 | -0.391068 | 1 | 0 |
| 7 | 0.064972 | -0.292924 | 1.874810 | 0.363856 | 1.341844 | 0.251944 | 1.013951 | 1.655213 | 0.115402 | 1.527592 | -0.325145 | 0.025517 | 1 | 0 |
| 8 | 0.457188 | -1.280226 | 0.508305 | 0.536904 | 2.008575 | 0.193572 | -0.498497 | 0.270656 | -1.409592 | -0.431942 | -1.096868 | -1.161518 | 2 | 0 |
| 9 | 0.264205 | -0.451954 | 0.540019 | -0.518915 | 0.759702 | 2.591054 | -0.083006 | 0.304222 | -0.558774 | 0.300192 | -0.287566 | -0.493630 | 2 | 0 |
| 10 | 1.615916 | -0.476284 | 0.254007 | -0.624451 | 0.097446 | 0.217831 | -0.040382 | 1.768809 | -0.319326 | 0.979400 | 0.016016 | -0.153245 | 2 | 0 |
| 11 | 0.834285 | -1.405123 | 0.704867 | -0.689665 | 2.008575 | 1.203152 | -1.002498 | -0.667904 | -1.688291 | -1.156923 | -0.737306 | -0.879992 | 2 | 0 |
| 12 | -0.368007 | 0.683133 | -0.474222 | 0.547694 | -0.820217 | 0.364380 | -0.474588 | -0.674021 | 1.523918 | -0.793291 | 1.078068 | -0.457193 | 0 | 0 |
| 13 | -0.839289 | 0.306633 | -0.853660 | 0.723116 | 0.435777 | -0.879183 | 0.335639 | 0.593469 | 0.708144 | -0.396821 | -0.242968 | 1.924655 | 3 | 0 |
| 14 | -0.319898 | 0.340082 | -0.491559 | 0.701985 | -0.842146 | 0.597909 | -0.644831 | -0.662251 | 1.523918 | -0.575211 | 0.534899 | -0.527162 | 0 | 0 |
| 15 | -0.609565 | 0.412613 | -0.695381 | 1.316178 | -0.719769 | 0.865430 | 1.679917 | -0.849334 | 1.523918 | -0.735830 | 0.144609 | 0.242574 | 0 | 0 |
| 16 | -0.294394 | 0.741619 | 0.151913 | 2.539991 | 0.416363 | 0.148867 | 1.677200 | 0.822220 | 1.003847 | -0.526991 | 0.785121 | -0.293878 | 5 | 0 |
| 17 | 1.116695 | -0.420423 | 1.149167 | 0.301950 | 0.500878 | 1.773601 | -0.060893 | 0.672677 | 0.289742 | 1.527593 | 1.283583 | 0.238430 | 5 | 0 |
| 18 | 0.842794 | -0.575729 | 0.643169 | -0.602296 | -0.331996 | 1.529202 | -0.867435 | 0.191979 | -0.217921 | 0.807237 | 1.699829 | 0.130123 | 5 | 0 |
| 19 | -0.030794 | 0.030288 | 0.462185 | -0.769191 | -0.543974 | 2.591054 | -0.487337 | -0.076347 | -0.991742 | 0.900076 | -0.242676 | -1.415815 | 2 | 0 |
| 20 | -1.324459 | 2.249029 | -1.202104 | -0.290955 | -0.053323 | -1.169169 | -0.449254 | -1.306597 | -0.432854 | -0.500100 | -1.455507 | 0.028032 | 4 | 0 |
| 21 | -1.391553 | 2.249029 | -1.278978 | -0.888575 | -0.811278 | -1.180328 | 0.198450 | -1.316982 | -0.927827 | -1.028770 | -1.496198 | 0.693363 | 4 | 0 |
| 22 | -0.662886 | 0.307712 | -0.805667 | 1.248429 | -0.021261 | -0.853836 | 0.505072 | -0.109644 | 0.239978 | -0.008774 | -0.437828 | 1.924655 | 3 | 0 |
| 23 | 1.105419 | -0.629615 | 0.090579 | 2.059265 | -0.742273 | 0.287915 | -0.889948 | 0.338598 | 0.769168 | 0.080426 | 1.699829 | -0.385717 | 5 | 0 |
| 24 | 1.043642 | -0.624779 | 0.129667 | 1.899266 | -0.669239 | 0.492505 | -0.813576 | 0.318395 | 0.576968 | -0.083727 | 1.699829 | -0.180141 | 5 | 0 |
| 25 | 0.301993 | -0.705708 | -0.070367 | 0.439151 | -0.783839 | 1.504692 | -0.883292 | -0.324238 | 0.394206 | -0.681481 | 1.699829 | -0.468423 | 5 | 0 |
| 26 | 1.404180 | -1.101276 | -1.004741 | 0.000759 | -1.064381 | -0.992797 | -0.971380 | 1.704361 | 0.968314 | 0.696274 | 1.699829 | -0.198305 | 5 | 0 |
| 27 | -0.634723 | -0.430657 | -0.787967 | 1.284818 | -0.780406 | -0.201832 | 0.438439 | -0.741240 | 1.523918 | -0.871410 | -0.336990 | -0.773167 | 0 | 0 |
| 28 | 0.317357 | -0.327927 | 0.301217 | -0.742175 | 0.030999 | -0.632488 | -0.114595 | 2.018795 | -0.481880 | 0.192970 | -0.259076 | 0.650325 | 2 | 0 |
| 29 | -0.648337 | -1.398422 | 0.413041 | -1.220330 | -0.442423 | -1.123348 | -0.408672 | 2.018795 | -1.250844 | -0.200036 | -1.302509 | -0.368660 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 186 | 0.393077 | -0.250074 | 0.038107 | -0.191832 | -0.290416 | 1.007564 | -0.703416 | -0.310414 | -0.798470 | -0.796349 | 1.699829 | -0.544372 | 5 | 1 |
| 187 | -1.377643 | -0.803877 | 2.211117 | -0.707481 | 1.879500 | 0.084271 | 0.023163 | -1.061854 | -1.515997 | 0.877885 | -1.504060 | -1.062191 | 1 | 1 |
| 188 | 1.615917 | 0.379972 | 0.224721 | -0.034053 | 1.116997 | 1.461618 | -0.476350 | 1.089453 | 0.375577 | -0.678774 | 0.197515 | -0.407654 | 5 | 1 |
| 189 | 1.615917 | -0.530983 | -0.059684 | -0.924859 | 0.297570 | -0.788527 | -0.863155 | 0.507705 | -1.231423 | 0.671175 | -1.167708 | -1.068322 | 2 | 1 |
| 190 | -0.315846 | -1.045971 | 1.094836 | -1.257540 | -0.646452 | -0.759760 | -1.119000 | 1.113215 | -1.727260 | 1.527592 | -1.499848 | -1.441971 | 2 | 1 |
| 191 | -0.183414 | -1.108627 | 0.780160 | -1.125961 | -0.797291 | -0.594726 | -1.084903 | -0.937498 | -1.226658 | 1.527592 | -1.097546 | -0.723262 | 2 | 1 |
| 192 | 1.615917 | -1.079754 | 0.603712 | -1.142855 | -0.595133 | -1.113955 | -1.106489 | 0.175898 | -1.304200 | -0.527112 | -1.252788 | -0.999291 | 2 | 1 |
| 193 | 1.615917 | -0.514731 | 1.852987 | -0.738263 | -0.091935 | 0.444710 | -0.919840 | 0.338934 | -0.676013 | 1.060982 | -0.689200 | -0.040139 | 2 | 1 |
| 194 | -1.023096 | -0.007046 | -0.867797 | -0.555651 | -0.693210 | -0.998105 | 0.001088 | -0.891619 | 1.203890 | -0.871836 | -0.513026 | 1.924654 | 3 | 1 |
| 195 | 0.242106 | -0.524011 | -0.569132 | -0.770429 | 1.699110 | -0.415382 | -0.903373 | -0.100685 | -0.974586 | 1.527592 | -0.629775 | -0.696446 | 2 | 1 |
| 196 | -1.369321 | -0.969464 | 2.211117 | -1.188189 | -0.580502 | -1.060826 | 0.122236 | -0.950228 | -1.726779 | 0.105209 | -1.501989 | -0.426102 | 1 | 1 |
| 197 | -1.015340 | -0.530076 | -0.283168 | -1.082866 | -0.835974 | -1.147408 | 0.419459 | -1.191474 | -0.661554 | 1.527592 | -0.997834 | 1.722317 | 3 | 1 |
| 198 | -0.850883 | -0.441126 | 0.243450 | 0.222028 | 1.249673 | -0.347006 | -0.107132 | -0.257125 | 0.126194 | 1.527592 | -0.422391 | 1.077974 | 3 | 1 |
| 199 | 1.615917 | -0.957115 | -0.818624 | -0.751346 | 0.920566 | 0.942750 | -0.885506 | 0.827017 | -0.967859 | 0.218694 | -0.831072 | 0.035852 | 2 | 1 |
| 200 | 1.615917 | -1.094018 | -1.161937 | -0.817956 | 0.985373 | 0.188513 | -0.904777 | 0.516967 | -1.230226 | 1.160960 | -0.827322 | 0.309158 | 2 | 1 |
| 201 | 1.615917 | -1.168115 | -1.198039 | -0.967002 | 1.353491 | 1.155611 | -0.998698 | 1.025620 | -1.193487 | -0.101131 | -1.079050 | 0.318717 | 2 | 1 |
| 202 | 0.887584 | -1.008954 | 1.434477 | -0.648944 | 1.812023 | -1.119854 | 0.217275 | 1.663147 | -1.635939 | -0.375805 | -1.213970 | 1.924655 | 1 | 1 |
| 203 | -0.281732 | -0.334833 | 0.143734 | -0.498620 | 0.200931 | -0.330363 | -0.167310 | 1.001306 | -0.272888 | 0.327019 | 1.070418 | 1.924654 | 3 | 1 |
| 204 | -0.135492 | -0.180497 | 0.349882 | -0.411950 | 0.523304 | -0.164545 | -0.419377 | 1.939169 | -0.073775 | 0.044241 | 1.347349 | 1.924654 | 3 | 1 |
| 205 | 1.615917 | -0.407161 | 1.545772 | 0.566414 | 0.881235 | -0.072638 | -0.448623 | 1.725723 | -0.162715 | 1.468765 | -0.660579 | -0.516106 | 2 | 1 |
| 206 | 1.404839 | -0.218548 | 0.899766 | -0.292046 | 1.212597 | 1.247130 | -0.134708 | 2.018795 | -0.672111 | 0.036232 | -0.857434 | -0.755932 | 2 | 1 |
| 207 | 1.593455 | -0.041945 | 1.395647 | -0.303439 | 1.481579 | 0.972266 | -0.240798 | 2.018795 | -0.629725 | -0.240328 | -0.765081 | -0.553387 | 2 | 1 |
| 208 | 1.395568 | 1.540836 | -0.840747 | 0.193399 | -0.891845 | 2.198621 | -0.578460 | -0.895624 | -0.218762 | -1.304397 | 1.699829 | -0.937437 | 0 | 1 |
| 209 | 1.583225 | 1.673175 | -1.151481 | -0.093932 | -1.043532 | 2.078341 | -1.020401 | -0.782673 | -0.238115 | -1.519520 | 1.699829 | -1.128162 | 0 | 1 |
| 210 | 1.370331 | 1.252913 | -1.149111 | 0.407119 | -0.751369 | 1.134063 | -0.818484 | -1.134854 | -0.210751 | -1.317205 | 1.699829 | -1.128242 | 0 | 1 |
| 211 | 1.041007 | -1.290910 | -1.008073 | -1.187425 | -0.642020 | -0.253467 | -1.115999 | -1.223980 | -1.338703 | 1.527592 | -0.933271 | -0.918089 | 2 | 1 |
| 212 | 1.615915 | -1.334768 | -0.029491 | -1.257929 | -0.839407 | -1.180328 | -1.119000 | 0.430783 | -1.413857 | -1.452312 | -1.464581 | -1.270209 | 2 | 1 |
| 213 | -0.744293 | 0.244034 | -1.095374 | -0.843102 | -0.676799 | -1.167238 | 0.315258 | -1.196862 | -0.503734 | 0.774377 | -0.704666 | 1.924655 | 3 | 1 |
| 214 | 0.406336 | 0.410463 | -0.317774 | 0.569095 | -0.738039 | 0.037857 | -0.548951 | -0.093707 | -0.061980 | -0.002270 | 1.699829 | 1.020268 | 5 | 1 |
| 215 | -1.011813 | -0.264498 | -0.827835 | -0.483839 | 1.047147 | -0.994911 | -0.261502 | -0.762674 | 0.219646 | 0.586379 | -0.527753 | 1.924654 | 3 | 1 |
216 rows × 14 columns
In [554]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[554]:
<matplotlib.axes._subplots.AxesSubplot at 0x1b8317bc588>
Random Forest¶
Arte Francés¶
In [555]:
X = df_n_ps_std[0].iloc[:,8:-1]
y = df_n_ps[0]['chosen']
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [556]:
from sklearn.ensemble import RandomForestClassifier
np.random.seed(1234)
rforest = RandomForestClassifier()
rforest.fit(X_train, y_train)
y_pred = rforest.predict(X_test)
cm= confusion_matrix(y_test, y_pred)
print("Exactitud: ", accuracy_score(y_test, y_pred))
print("Kappa : ", cohen_kappa_score(y_test, y_pred))
cm
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release. from numpy.core.umath_tests import inner1d
Exactitud: 0.7848101265822784 Kappa : 0.3458353628835851
Out[556]:
array([[55, 2],
[15, 7]], dtype=int64)
In [557]:
y_pred
Out[557]:
array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0], dtype=int64)
In [558]:
rforest.oob_score_
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-558-69c072cb9288> in <module> ----> 1 rforest.oob_score_ AttributeError: 'RandomForestClassifier' object has no attribute 'oob_score_'
In [ ]:
X.columns[np.argsort(-rforest.feature_importances_)]
In [ ]:
-rforest.feature_importances_
In [ ]:
indices = np.argsort(-rforest.feature_importances_)#[::-1]
variables = [X.columns[i] for i in indices]
plt.figure(figsize=(16,8))
plt.title("Importancia de las variables")
plt.bar(range(X.shape[1]), rforest.feature_importances_[indices])
plt.xticks(range(X.shape[1]), variables, rotation=90)
plt.show()
ANN¶
In [ ]:
X = df_n_ps_[0]
In [ ]:
y = df_n_ps[0]['chosen']
In [ ]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [ ]:
X_train.shape
In [ ]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [ ]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [ ]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [ ]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
In [ ]:
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [ ]:
input_tensor = Input(shape = (n0,))
In [ ]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [ ]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [ ]:
model.summary()
In [ ]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
In [ ]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
In [ ]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
In [ ]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
In [ ]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
KMeans¶
In [ ]:
X
In [ ]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
In [ ]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
K=3
In [ ]:
kmeans_ch = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_ch.fit(X)
In [ ]:
kmeans_ch.labels_
In [ ]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
In [ ]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [ ]:
X
In [ ]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
In [ ]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[1]))
ANN¶
In [ ]:
X = df_n_ps_std_ch[1]
In [ ]:
y = df_n_ps[1]['chosen']
In [ ]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [ ]:
X_train.shape
In [ ]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [ ]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [ ]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [ ]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
In [ ]:
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [ ]:
input_tensor = Input(shape = (n0,))
In [ ]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [ ]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [ ]:
model.summary()
In [ ]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
In [ ]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
In [ ]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
In [ ]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
In [ ]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
KMeans¶
In [ ]:
X
In [ ]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
In [ ]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
K=2
In [ ]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
In [ ]:
kmeans_ch.labels_
In [ ]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
In [ ]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [ ]:
X
In [ ]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
In [ ]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[2]))
ANN¶
In [ ]:
X = df_n_ps_std_ch[2]
In [ ]:
y = df_n_ps[2]['chosen']
In [ ]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [ ]:
X_train.shape
In [ ]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [ ]:
activation_vec = ['logistic', 'relu', grid.best_params_['activation']]
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [ ]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [ ]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
In [ ]:
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [ ]:
input_tensor = Input(shape = (n0,))
In [ ]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [ ]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [ ]:
model.summary()
In [ ]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
In [ ]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
In [ ]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
In [ ]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
In [ ]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
KMeans¶
In [ ]:
X
In [ ]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
In [ ]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
K=2
In [ ]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
In [ ]:
kmeans_ch.labels_
In [ ]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
In [ ]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [ ]:
X
In [ ]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
In [ ]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[3]))
ANN¶
In [ ]:
X = df_n_ps_std_ch[3]
In [ ]:
y = df_n_ps[3]['chosen']
In [ ]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [ ]:
X_train.shape
In [ ]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [ ]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [ ]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [ ]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
In [ ]:
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [ ]:
input_tensor = Input(shape = (n0,))
In [ ]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [ ]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [ ]:
model.summary()
In [ ]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
In [ ]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
In [ ]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
In [ ]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
In [ ]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
KMeans¶
In [ ]:
X
In [ ]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
In [ ]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
K=2
In [ ]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
In [ ]:
kmeans_ch.labels_
In [ ]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
In [ ]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [ ]:
X
In [ ]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
In [ ]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[4]))
ANN¶
In [ ]:
X = df_n_ps_std_ch[4]
In [ ]:
y = df_n_ps[4]['chosen']
In [ ]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [ ]:
X_train.shape
In [ ]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
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activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
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import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
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grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
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n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
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input_tensor = Input(shape = (n0,))
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hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
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model = Model([input_tensor], [classification_output])
weights = model.get_weights()
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model.summary()
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model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
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acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
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test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
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y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
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y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
KMeans¶
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X
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WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
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plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
K=2
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kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
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kmeans_ch.labels_
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clusters_ch = kmeans_ch.predict(X)
clusters_ch
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X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
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X
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stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
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from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[5]))
ANN¶
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X = df_n_ps_std_ch[5]
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y = df_n_ps[5]['chosen']
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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X_train.shape
In [ ]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [ ]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [ ]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
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grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
In [ ]:
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
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input_tensor = Input(shape = (n0,))
In [ ]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = grid.best_params_['activation'])(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
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model = Model([input_tensor], [classification_output])
weights = model.get_weights()
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model.summary()
In [ ]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
In [ ]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
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test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
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y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
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y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
KMeans¶
In [ ]:
X
In [ ]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
In [ ]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
K=6
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kmeans_ch = KMeans(n_clusters=6, random_state=0, n_init=10)
kmeans_ch.fit(X)
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kmeans_ch.labels_
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clusters_ch = kmeans_ch.predict(X)
clusters_ch
In [ ]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [ ]:
X
In [ ]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
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In [ ]:
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